Abstract: We here present the Forward Planning Situated Protocol (FPSP), for scalable, energy efficient and fault tolerant data propagation in situated wireless sensor networks. To deal with the increased complexity of such deeply networked sensor systems, instead of emphasizing on a particular aspect of the services provided, i.e. either for low-energy periodic, or low-latency event-driven, or high-success query-based sensing, FPSP uses two novel mechanisms that allow the network operator to adjust the performance of the protocol in terms of energy, latency and success rate on a per-task basis. We emphasize on distributedness, direct or indirect interactions among relatively simple agents, flexibility and robustness.
The protocol operates by employing a series of plan & forward phases through which devices self-organize into forwarding groups that propagate data over discovered paths. FPSP performs a limited number of long range, high power data transmissions to collect information regarding the neighboring devices. The acquired information, allows to plan a (parameterizable long by {\"e}) sequence of short range, low power transmissions between nearby particles, based on certain optimization criteria. All particles that decide to respond (based on local criteria) to these long range transmissions enter the forwarding phase during which information is propagated via the acquired plan. Clearly, the duration of the forwarding phases is characterized by the parameter {\"e}, the transmission medium and the processing speed of the devices. In fact the parameter {\"e} provides a mechanism to adjust the protocol performance in terms of the latency--energy trade-off. By reducing {\"e} the latency is reduced at the cost of spending extra energy, while by increasing {\"e}, the energy dissipation is reduced but the latency is increased.
To control the success rate--energy trade-off, particles react locally on environment and context changes by using a set of rules that are based on response thresholds that relate individual-level plasticity with network-level resiliency, motivated by the nature-inspired method for dividing labor, a metaphor of social insect behavior for solving problems [1]. Each particle has an individual response threshold {\`E} that is related to the "local" density (as observed by the particle, [2]); particles engage in propagation of events when the level of the task-associated stimuli exceeds their thresholds. Let s be the intensity of a stimulus associated with a particular sensing task, set by the human authorities. We adopt the response function T_{{\`e}}(s) = s^{n}over s^{n} + {\`e}^{n}, the probability of performing the task as a function of s, where n > 1 determines the steepness of the threshold. Thus, when {\`e} is small (i.e. the network is sparse) then the response probability increases; when s increases (i.e. for critical sensing tasks) the response probability increases as well.
This role-based approach where a selective number of devices do the high cost planning and the rest of the network operates in a low cost state leads to systems that have increased energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).

Abstract: Wireless Sensor Networks are complex systems consisting of a number of relatively simple autonomous sensing devices spread on a geographical area. The peculiarity of these devices lies on the constraints they face in relation to their energy reserves and their computational, storage and communication capabilities. The utility of these sensors is to measure certain environmental conditions and to detect critical events in relation to these measurements. Those events thereupon have to be reported to a specific central station namely the “sink”. This data propagation generally has the form of a hop-by-hop transmission. In this framework we work on distributed data propagation protocols which are taking into account the energy reserves of the sensors. In particular following the work of Chatzigiannakis et al. on the Probabilistic Forwarding Protocol (PFR) we present the distributed probabilistic protocol EFPFR, which favors transmission from the less depleted sensors in addition to favor transmissions close to the “optimal line”. This protocol is simple and relies only on local information for propagation decisions. Its main goal is to limit the total amount of energy dissipated per event and therefore to extend the network’s operation duration.

Abstract: In this work, we study the propagation of influence and computation in dynamic distributed computing systems that are possibly disconnected at every instant. We focus on a synchronous message-passing communication model with broadcast and bidirectional links. Our network dynamicity assumption is a worst-case dynamicity controlled by an adversary scheduler, which has received much attention recently. We replace the usual (in worst-case dynamic networks) assumption that the network is connected at every instant by minimal temporal connectivity conditions. Our conditions only require that another causal influence occurs within every time window of some given length. Based on this basic idea, we define several novel metrics for capturing the speed of information spreading in a dynamic network. We present several results that correlate these metrics. Moreover, we investigate termination criteria in networks in which an upper bound on any of these metrics is known. We exploit our termination criteria to provide efficient (and optimal in some cases) protocols that solve the fundamental counting and all-to-all token dissemination (or gossip) problems.

Abstract: In this chapter, our focus is on computational network analysis from a theoretical point of view. In particular, we study the \emph{propagation of influence and computation in dynamic distributed computing systems}. We focus on a \emph{synchronous message passing} communication model with bidirectional links. Our network dynamicity assumption is a \emph{worst-case dynamicity} controlled by an adversary scheduler, which has received much attention recently. We first study the fundamental \emph{naming} and \emph{counting} problems (and some variations) in
networks that are \emph{anonymous}, \emph{unknown}, and possibly dynamic. Network dynamicity is modeled here by the \emph{1-interval connectivity model}, in which communication is synchronous and a (worst-case) adversary
chooses the edges of every round subject to the condition that each instance is connected. We then replace this quite strong assumption by minimal \emph{temporal connectivity} conditions. These conditions only require that \emph{another causal influence occurs within every time-window of some given length}. Based on this basic idea we define several novel metrics for capturing the speed of information spreading in a dynamic network. We present several results that correlate these metrics. Moreover, we investigate \emph{termination criteria} in networks in which an upper bound on any of these metrics is known. We exploit these termination criteria to provide efficient (and optimal in some cases) protocols that solve the fundamental \emph{counting} and \emph{all-to-all token dissemination} (or \emph{gossip}) problems. Finally, we propose another model of worst-case temporal connectivity, called \emph{local
communication windows}, that assumes a fixed underlying communication network and restricts the adversary to allow communication between local neighborhoods in every time-window of some fixed length. We prove some basic properties and provide a protocol for counting in this model.

Abstract: Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for the efficient deployment and access of emerging internet-scale information systems. Examples of such applications range from optimizing query access plans in internet-scale databases, to evaluating the significance (rank/score) of various data items in information retrieval applications. The key constraints that any acceptable solution must satisfy are: (i) efficiency: the number of nodes that need be contacted for counting purposes must be small in order to enjoy small latency and bandwidth requirements; (ii) scalability, seemingly contradicting the efficiency goal: arbitrarily large numbers of nodes nay need to add elements to a (multi-) set, which dictates the need for a highly distributed solution, avoiding server-based scalability, bottleneck, and availability problems; (iii) access and storage load balancing: counting and related overhead chores should be distributed fairly to the nodes of the network; (iv) accuracy: tunable, robust (in the presence of dynamics and failures) and highly accurate cardinality estimation; (v) simplicity and ease of integration: special, solution-specific indexing structures should be avoided. In this paper, first we contribute a highly-distributed, scalable, efficient, and accurate (multi-) set cardinality estimator. Subsequently, we show how to use our solution to build and maintain histograms, which have been a basic building block for query optimization for centralized databases, facilitating their porting into the realm of internet-scale data networks.

Abstract: This paper deals with systems of multiple mobile robots each of which observes the positions of the other robots and moves to a new position so that eventually the robots form a circle. In the model we study, the robots are anonymous and oblivious, in the sense that they cannot be distinguished by their appearance and do not have a common x-y coordinate system, while they are unable to remember past actions.
We propose a new distributed algorithm for circle formation on the plane. We prove that our algorithm is correct and provide an upper bound for its performance. In addition, we conduct an extensive and detailed comparative simulation experimental study with the DK algorithm described in [7]. The results show that our algorithm is very simple and takes considerably less time to execute than algorithm DK.

Abstract: This paper deals with systems of multiple mobile robots each of which observes the positions of the other robots and moves to a new position so that eventually the robots form a circle. In the model we study, the robots are anonymous and oblivious, in the sense that they cannot be distinguished by their appearance and do not have a common x-y coordinate system, while they are unable to remember past actions.
We propose a new distributed algorithm for circle formation on the plane. We prove that our algorithm is correct and provide an upper bound for its performance. In addition, we conduct an extensive and detailed comparative simulation experimental study with the DK algorithm. The results show that our algorithm is very simple and takes considerably less time to execute than algorithm DK.

Abstract: Counting items in a distributed system, and estimating the cardinality of multisets in particular,
is important for a large variety of applications and a fundamental building block for emerging Internet-scale information systems. Examples of such applications range from optimizing query access plans in peer-to-peer data sharing, to computing the significance (rank/score) of data items in distributed information retrieval. The general formal problem addressed in this article is computing the network-wide distinct number of items with some property (e.g., distinct files with file name
containing “spiderman”) where each node in the network holds an arbitrary subset, possibly overlapping the subsets of other nodes. The key requirements that a viable approach must satisfy are:
(1) scalability towards very large network size, (2) efficiency regarding messaging overhead, (3) load
balance of storage and access, (4) accuracy of the cardinality estimation, and (5) simplicity and easy
integration in applications. This article contributes the DHS (Distributed Hash Sketches) method
for this problem setting: a distributed, scalable, efficient, and accurate multiset cardinality estimator.
DHSis based on hash sketches for probabilistic counting, but distributes the bits of each counter
across network nodes in a judicious manner based on principles of Distributed Hash Tables, paying
careful attention to fast access and aggregation as well as update costs. The article discusses various
design choices, exhibiting tunable trade-offs between estimation accuracy, hop-count efficiency, and
load distribution fairness. We further contribute a full-fledged, publicly available, open-source implementation of all our methods, and a comprehensive experimental evaluation for various settings.

Abstract: In this paper we describe a new simulation platform for heterogeneous distributedsystems comprised of small programmable objects (e.g., wireless sensor networks) and traditional networked processors. Simulating such systems is complicated because of the need to coordinate compilers and simulators, often with very different interfaces, options, and fidelities.
Our platform (which we call ADAPT) is a flexible and extensible environment that provides a highly scalable simulator with unique characteristics. While the platform provides advanced functionality such as real-time simulation monitoring, custom topologies and scenarios, mixing real and simulated nodes, etc., the effort required by the user and the impact to her code is minimal. We here present its architecture, the most important design decisions, and discuss its distinct features and functionalities. We integrate our simulator to the Sun SPOT platform to enable simulation of sensing applications that employ both low-end and high-end devices programmed with different languages that are internetworked with heterogeneous technologies. We believe that ADAPT will make the development of applications that use small programmable objects more widely accessible and will enable researchers to conduct a joint research approach that combines both theory and practice.

Abstract: In this paper we present the design of a simulator platform called FUSE (Fast Universal Simulator Engine). The term Universal means that the Engine can be adapted easily to different domains and be used for varying simulation needs, although our main target is simulation of distributed algorithms in distributed computing environments. The Engine is Fast in the sense that the simulation overhead is minimal and very large systems can be simulated. We discuss the architecture and the design decisions that form the basis of these features. We also describe the functionality that is provided to its users (e.g., monitoring, statistics, etc.).

Abstract: Distributed algorithm designers often assume that system processes execute the same predefined software. Alternatively, when they do not assume that, designers turn to non-cooperative games and seek an outcome that corresponds to a rough consensus when no coordination is allowed. We argue that both assumptions are inapplicable in many real distributedsystems, e.g., the Internet, and propose designing self-stabilizing and Byzantine fault-tolerant distributed game authorities. Once established, the game authority can secure the execution of any complete information game. As a result, we reduce costs that are due to the processes˘ freedom of choice. Namely, we reduce the price of malice.

Abstract: Information retrieval (IR) in peer-to-peer (P2P) networks,
where the corpus is spread across many loosely coupled
peers, has recently gained importance. In contrast to IR
systems on a centralized server or server farm, P2P IR faces
the additional challenge of either being oblivious to global
corpus statistics or having to compute the global measures
from local statistics at the individual peers in an efficient,
distributed manner. One specific measure of interest is the
global document frequency for different terms, which would
be very beneficial as term-specific weights in the scoring and
ranking of merged search results that have been obtained
from different peers.
This paper presents an efficient solution for the problem
of estimating global document frequencies in a large-scale
P2P network with very high dynamics where peers can join
and leave the network on short notice. In particular, the
developed method takes into account the fact that the lo-
cal document collections of autonomous peers may arbitrar-
ily overlap, so that global counting needs to be duplicate-
insensitive. The method is based on hash sketches as a
technique for compact data synopses. Experimental stud-
ies demonstrate the estimator?s accuracy, scalability, and
ability to cope with high dynamics. Moreover, the benefit
for ranking P2P search results is shown by experiments with
real-world Web data and queries.

Abstract: With this work we aim to make a three-fold contribution.
We first address the issue of supporting efficiently queries
over string-attributes involving prefix, suffix, containment,
and equality operators in large-scale data networks. Our
first design decision is to employ distributed hash tables
(DHTs) for the data network?s topology, harnessing their
desirable properties. Our next design decision is to derive
DHT-independent solutions, treating DHT as a black box.
Second, we exploit this infrastructure to develop efficient
content based publish/subscribe systems. The main con-
tribution here are algorithms for the efficient processing of
queries (subscriptions) and events (publications). Specifi-
cally, we show that our subscription processing algorithms
require O(logN) messages for a N-node network, and our
event processing algorithms require O(l ? logN) messages
(with l being the average string length).
Third, we develop algorithms for optimizing the proces-
sing of multi-dimensional events, involving several string at-
tributes. Further to our analysis, we provide simulation-
based experiments showing promising performance results
in terms of number of messages, required bandwidth, load
balancing, and response times.

Abstract: Information retrieval (IR) in peer-to-peer (P2P) networks,
where the corpus is spread across many loosely coupled
peers, has recently gained importance. In contrast to IR
systems on a centralized server or server farm, P2P IR faces
the additional challenge of either being oblivious to global
corpus statistics or having to compute the global measures
from local statistics at the individual peers in an efficient,
distributed manner. One specific measure of interest is the
global document frequency for different terms, which would
be very beneficial as term-specific weights in the scoring and
ranking of merged search results that have been obtained
from different peers.
This paper presents an efficient solution for the problem
of estimating global document frequencies in a large-scale
P2P network with very high dynamics where peers can join
and leave the network on short notice. In particular, the
developed method takes into account the fact that the lo-
cal document collections of autonomous peers may arbitrar-
ily overlap, so that global counting needs to be duplicate-
insensitive. The method is based on hash sketches as a
technique for compact data synopses. Experimental stud-
ies demonstrate the estimator?s accuracy, scalability, and
ability to cope with high dynamics. Moreover, the benefit
for ranking P2P search results is shown by experiments with
real-world Web data and queries.

Abstract: We address the issue of measuring storage, or query load distribution fairness in peer-to-peer data management systems. Existing metrics may look promising from the point of view of specific peers, while in reality being far from optimal from a global perspective. Thus, first we define the requirements and study the appropriateness of various statistical metrics for measuring load distribution fairness towards these requirements. The metric proposed as most appropriate is the Gini coefficient (G). Second, we develop novel distributed sampling algorithms to compute G on-line, with high precision, efficiently, and scalably. Third, we show how G can readily be utilized on-line by higher-level algorithms which can now know when to best intervene to correct load imbalances. Our analysis and experiments testify for the efficiency and accuracy of these algorithms, permitting the online use of a rich and reliable metric, conveying a global perspective of the distribution.

Abstract: Recent rapid developments in micro-electro-mechanical systems
(MEMS), wireless communications and digital electronics have already
led to the development of tiny, low-power, low-cost sensor devices.
Such devices integrate sensing, limited data processing and restricted
communication capabilities.
Each sensor device individually might have small utility, however the
effective distributed co-ordination of large numbers of such devices can
lead to the efficient accomplishment of large sensing tasks. Large numbers
of sensors can be deployed in areas of interest (such as inaccessible
terrains or disaster places) and use self-organization and collaborative
methods to form an ad-hoc network.
We note however that the efficient and robust realization of such large,
highly-dynamic, complex, non-conventional networking environments is
a challenging technological and algorithmic task, because of the unique
characteristics and severe limitations of these devices.
This talk will present and discuss several important aspects of the
design, deployment and operation of sensor networks. In particular, we
provide a brief description of the technical specifications of state-of-theart
sensor, a discussion of possible models used to abstract such networks,
a discussion of some key algorithmic design techniques (like randomization,
adaptation and hybrid schemes), a presentation of representative
protocols for sensor networks, for important problems including data
propagation, collision avoidance and energy balance and an evaluation
of crucial performance properties (correctness, efficiency, fault-tolerance)
of these protocols, both with analytic and simulation means.

Abstract: Let n atomic players be routing their unsplitable flow on mresources.
When each player has the option to drop her current resource and select a better
one, and this option is exercised sequentially and unilaterally, then a Nash Equilibrium
(NE) will be eventually reached. Acting sequentially, however, is unrealistic
in large systems. But, allowing concurrency, with an arbitrary number of
players updating their resources at each time point, leads to an oscillation away
from NE, due to big groups of players moving simultaneously and due to nonsmooth
resource cost functions. In this work, we validate experimentally simple
concurrent protocols that are realistic, distributed and myopic yet are scalable, require
only information local at each resource and, still, are experimentally shown
to quickly reach a NE for a range of arbitrary cost functions.

Abstract: Wireless Sensor Networks (WSNs) constitute a recent and promising new
technology that is widely applicable. Due to the applicability of this
technology and its obvious importance for the modern distributed
computational world, the formal scientific foundation of its inherent laws
becomes essential. As a result, many new computational models for WSNs
have been proposed. Population Protocols (PPs) are a special category of
such systems. These are mainly identified by three distinctive
characteristics: the sensor nodes (agents) move passively, that is, they
cannot control the underlying mobility pattern, the available memory to
each agent is restricted, and the agents interact in pairs. It has been
proven that a predicate is computable by the PP model iff it is
semilinear. The class of semilinear predicates is a fairly small class. In
this work, our basic goal is to enhance the PP model in order to improve
the computational power. We first make the assumption that not only the
nodes but also the edges of the communication graph can store restricted
states. In a complete graph of n nodes it is like having added O(n2)
additional memory cells which are only read and written by the endpoints
of the corresponding edge. We prove that the new model, called Mediated
Population Protocol model, can operate as a distributed nondeterministic
Turing machine (TM) that uses all the available memory. The only
difference from a usual TM is that this one computes only symmetric
languages. More formally, we establish that a predicate is computable by
the new model iff it is symmetric and belongs to NSPACE(n2). Moreover, we
study the ability of the new model to decide graph languages (for general
graphs). The next step is to ignore the states of the edges and provide
another enhancement straight away from the PP model. The assumption now is
that the agents are multitape TMs equipped with infinite memory, that can
perform internal computation and interact with other agents, and we define
space-bounded computations. We call this the Passively mobile Machines
model. We prove that if each agent uses at most f(n) memory for f(n)={\`U}(log
n) then a predicate is computable iff it is symmetric and belongs to
NSPACE(nf(n)). We also show that this is not the case for f(n)=o(log n).
Based on these, we show that for f(n)={\`U}(log n) there exists a space
hierarchy like the one for classical symmetric TMs. We also show that the
latter is not the case for f(n)=o(loglog n), since here the corresponding
class collapses in the class of semilinear predicates and finally that for
f(n)={\`U}(loglog n) the class becomes a proper superset of semilinear
predicates. We leave open the problem of characterizing the classes for
f(n)={\`U}(loglog n) and f(n)=o(log n).

Abstract: Managing corporate Information Technology (IT) environment becomes increasingly complex as server logic architecture becomes distributed and the number of manageable entities increases. At the same time, the open source community has not yet produced a reliable systems and network management solution, even though there are open source initiatives specializing in individual fields of remote management. This paper presents OpenRSM, an integrated remote management system created by integrating individual open source initiatives and augmenting them to support additional functionality so that a lightweight integrated systems and network management solution is produced.

Abstract: The proliferation of peertopeer
(P2P) systems has come with various
compelling applications including file sharing based on distributed
hash tables (DHTs) or other kinds of overlay networks.
Searching the content of files (especially Web Search) requires
multikeyword
querying with scoring and ranking. Existing approaches
have no way of taking into account the correlation between
the keywords in the query. This paper presents our solution
that incorporates the queries and behavior of the users in the P2P
network such that interesting correlations can be inferred.

Abstract: Distributed algorithm designers often assume that system processes execute the same predefined software. Alternatively, when they do not assume that, designers turn to non-cooperative games and seek an outcome that corresponds to a rough consensus when no coordination is allowed. We argue that both assumptions are inapplicable in many real distributedsystems, e.g., the Internet, and propose designing self-stabilizing and Byzantine fault-tolerant distributed game authorities. Once established, the game authority can secure the execution of any complete information game. As a result, we reduce costs that are due to the processes˘ freedom of choice. Namely, we reduce the price of malice.

Abstract: In this paper we present an efficient general simulation strategy for
computations designed for fully operational BSP machines of n ideal processors,
on n-processor dynamic-fault-prone BSP machines. The fault occurrences are failstop
and fully dynamic, i.e., they are allowed to happen on-line at any point of the
computation, subject to the constraint that the total number of faulty processors
may never exceed a known fraction. The computational paradigm can be exploited
for robust computations over virtual parallel settings with a volatile underlying
infrastructure, such as a NETWORK OF WORKSTATIONS (where workstations may be
taken out of the virtual parallel machine by their owner).
Our simulation strategy is Las Vegas (i.e., it may never fail, due to backtracking
operations to robustly stored instances of the computation, in case of locally
unrecoverable situations). It adopts an adaptive balancing scheme of the workload
among the currently live processors of the BSP machine.
Our strategy is efficient in the sense that, compared with an optimal off-line
adversarial computation under the same sequence of fault occurrences, it achieves an O
ˇ
.log n ˘ log log n/2˘
multiplicative factor times the optimal work (namely, this
measure is in the sense of the “competitive ratio” of on-line analysis). In addition,
our scheme is modular, integrated, and considers many implementation points.
We comment that, to our knowledge, no previous work on robust parallel computations
has considered fully dynamic faults in the BSP model, or in general distributed
memory systems. Furthermore, this is the first time an efficient Las Vegas
simulation in this area is achieved.

Abstract: In this Phd thesis,, we try to use formal logic and threshold phenomena that asymptotically emerge with certainty in order to build new trust models and to evaluate the existing one. The departure point of our work is that dynamic, global computing systems are not amenable to a static viewpoint of the trust concept, no matter how this concept is formalized. We believe that trust should be a statistical, asymptotic concept to be studied in the limit as the system's components grow according to some growth rate. Thus, our main goal is to define trust as an emerging system property that ``appears'' or "disappears" when a set of properties hold, asymptotically with probability$ 0$ or $1$ correspondingly . Here we try to combine first and second order logic in order to analyze the trust measures of specific network models. Moreover we can use formal logic in order to determine whether generic reliability trust models provide a method for deriving trust between peers/entities as the network's components grow. Our approach can be used in a wide range of applications, such as monitoring the behavior of peers, providing a measure of trust between them, assessing the level of reliability of peers in a network. Wireless sensor networks are comprised of a vast number of ultra-small autonomous computing, communication and sensing devices, with restricted energy and computing capabilities, that co-operate to accomplish a large sensing task. Sensor networks can be very useful in practice. Such systems should at least guarantee the confidentiality and integrity of the information reported to the controlling authorities regarding the realization of environmental events. Therefore, key establishment is critical for the protection in wireless sensor networks and the prevention of adversaries from attacking the network. Finally in this dissertation we also propose three distributed group key establishment protocols suitable for such energy constrained networks. This dissertation is composed of two parts. Part I develops the theory of the first and second order logic of graphs - their definition, and the analysis of their properties that are expressible in the {\em first order language} of graphs. In part II we introduce some new distributed group key establishment protocols suitable for sensor networks. Several key establishment schemes are derived and their performance is demonstrated.

Abstract: Efficient query processing in traditional database
management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributedsystems, on the other hand, statisticsmanagement still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured
overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge.
To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation
of these tools for distributed statistical synopses,
and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability.

Abstract: In this paper we describe a new simulation platform for complex wireless sensor networks that operate a collection of distributed algorithms and network protocols. Simulating such systems is complicated because of the need to coordinate different network layers and debug protocol stacks, often with very different interfaces, options, and fidelities. Our platform (which we call WSNGE) is a flexible and extensible environment that provides a highly scalable simulator with unique characteristics. It focuses on user friendliness, providing every function in both scriptable and visual way, allowing the researcher to define simulations and view results in an easy to use graphical environment. Unlike other solutions, WSNGE does not distinguish between different scenario types, allowing multiple different protocols to run at the same time. It enables rich online interaction with running simulations, allowing parameters, topologies or the whole scenario to be altered at any point in time.