Abstract: We study the problem of greedy, single path data propaga-
tion in wireless sensor networks, aiming mainly to minimize
the energy dissipation. In particular, we rst mathemat-
ically analyze and experimentally evaluate the energy e-
ciency and latency of three characteristic protocols, each one
selecting the next hop node with respect to a di erent cri-
terion (minimum projection, minimum angle and minimum
distance to the destination). Our analytic and simulation
ndings suggest that any single criterion does not simulta-
neously satisfy both energy eciency and low latency. To-
wards parameterized energy-latency trade-o s we provide as
well hybrid combinations of the two criteria (direction and
proximity to the sink). Our hybrid protocols achieve sig-
ni cant perfomance gains and allow ne-tuning of desired
performance. Also, they have nice energy balance proper-
ties, and can prolong the network lifetime.
Abstract: Through recent technology advances in the eld of wireless energy transmission, Wireless Rechargeable Sensor Networks
(WRSN) have emerged. In this new paradigm for
WSNs a mobile entity called Mobile Charger (MC) traverses
the network and replenishes the dissipated energy of sensors.
In this work we rst provide a formal de nition of the charging
dispatch decision problem and prove its computational
hardness. We then investigate how to optimize the tradeo
s of several critical aspects of the charging process such
as a) the trajectory of the charger, b) the di erent charging
policies and c) the impact of the ratio of the energy
the MC may deliver to the sensors over the total available
energy in the network. In the light of these optimizations,
we then study the impact of the charging process to the
network lifetime for three characteristic underlying routing
protocols; a greedy protocol, a clustering protocol and an
energy balancing protocol. Finally, we propose a Mobile
Charging Protocol that locally adapts the circular trajectory
of the MC to the energy dissipation rate of each sub-region
of the network. We compare this protocol against several
MC trajectories for all three routing families by a detailed
experimental evaluation. The derived ndings demonstrate
signi cant performance gains, both with respect to the no
charger case as well as the di erent charging alternatives; in
particular, the performance improvements include the network
lifetime, as well as connectivity, coverage and energy
balance properties.
Abstract: In this work, we study the impact of the dynamic changing of the network link capacities on the stability properties of packet-switched networks. Especially, we consider the Adversarial, Quasi-Static Queuing Theory model, where each link capacity may take on only two possible (integer) values, namely 1 and C>1 under a (w,\~{n})-adversary. We obtain the following results:
• Allowing such dynamic changes to the link capacities of a network with just ten nodes that uses the LIS (Longest-in-System) protocol for contention–resolution results in instability at rates View the MathML source and for large enough values of C.
• The combination of dynamically changing link capacities with compositions of contention–resolution protocols on network queues suffices for similar instability bounds: The composition of LIS with any of SIS (Shortest-in-System), NTS (Nearest-to-Source), and FTG (Furthest-to-Go) protocols is unstable at rates View the MathML source for large enough values of C.
• The instability bound of the network subgraphs that are forbidden for stability is affected by the dynamic changes to the link capacities: we present improved instability bounds for all the directed subgraphs that were known to be forbidden for stability on networks running a certain greedy protocol.
Abstract: In this work, we study the impact of dynamically changing
link capacities on the delay bounds of LIS (Longest-In-
System) and SIS (Shortest-In-System) protocols on specific
networks (that can be modelled as Directed Acyclic Graphs-
DAGs) and stability bounds of greedy contention-resolution
protocols running on arbitrary networks under the Adversarial
Queueing Theory. Especially, we consider the model
of dynamic capacities, where each link capacity may take
on integer values from [1, C] withC > 1, under a (w, \~{n})-
adversary.
Abstract: In this work, we study the impact of dynamically changing link capacities on the delay bounds of LIS (Longest-In-System) and SIS (Shortest-In-System) protocols on specific networks (that can be modelled as Directed Acyclic Graphs (DAGs)) and stability bounds of greedy contention–resolution protocols running on arbitrary networks under the Adversarial Queueing Theory. Especially, we consider the model of dynamic capacities, where each link capacity may take on integer values from [1,C] with C>1, under a (w,\~{n})-adversary. We show that the packet delay on DAGs for LIS is upper bounded by O(iw\~{n}C) and lower bounded by {\`U}(iw\~{n}C) where i is the level of a node in a DAG (the length of the longest path leading to node v when nodes are ordered by the topological order induced by the graph). In a similar way, we show that the performance of SIS on DAGs is lower bounded by {\`U}(iw\~{n}C), while the existence of a polynomial upper bound for packet delay on DAGs when SIS is used for contention–resolution remains an open problem. We prove that every queueing network running a greedy contention–resolution protocol is stable for a rate not exceeding a particular stability threshold, depending on C and the length of the longest path in the network.
Abstract: A packet-switching network is stable if the number of packets in the network remains bounded at all times. A very natural question that arises in the context of stability properties of such networks is how network structure precisely affects these properties. In this work we embark on a systematic study of this question in the context of Adversarial Queueing Theory, which assumes that packets are adversarially injected into the network. We consider size, diameter, maximum vertex degree, minimum number of disjoint paths that cover all edges of the network and network subgraphs as crucial structural parameters of the network, and we present a comprehensive collection of structural results, in the form of stability and instability bounds on injection rate of the adversary for various greedyprotocols: —Increasing the size of a network may result in dropping its instability bound. This is shown through a novel, yet simple and natural, combinatorial construction of a size-parameterized network on which certain compositions of greedyprotocols are running. The convergence of the drop to 0.5 is found to be fast with and proportional to the increase in size. —Maintaining the size of a network small may already suffice to drop its instability bound to a substantially low value. This is shown through a construction of a FIFO network with size 22, which becomes unstable at rate 0.704. This represents the current state-of-the-art trade-off between network size and instability bound. —The diameter, maximum vertex degree and minimum number of edge-disjoint paths that cover a network may be used as control parameters for the stability bound of the network. This is shown through an improved analysis of the stability bound of any arbitrary FIFO network, which takes these parameters into account. —How much can network subgraphs that are forbidden for stability affect the instability bound? Through improved combinatorial constructions of networks and executions, we improve the state-of-the-art instability bound induced by certain known forbidden subgraphs on networks running a certain greedy protocol. —Our results shed more light and contribute significantly to a finer understanding of the impact of structural parameters on stability and instability properties of networks.
Abstract: In this work, we study the impact of the dynamic changing of the network link capacities on the stability properties of packet-switched networks. Especially, we consider the Adversarial, Quasi-Static Queuing Theory model, where each link capacity may take on only two possible (integer) values, namely 1 and C>1 under a (w,\~{n})-adversary. We obtain the following results:
• Allowing such dynamic changes to the link capacities of a network with just ten nodes that uses the LIS (Longest-in-System) protocol for contention–resolution results in instability at rates View the MathML source and for large enough values of C.
• The combination of dynamically changing link capacities with compositions of contention–resolution protocols on network queues suffices for similar instability bounds: The composition of LIS with any of SIS (Shortest-in-System), NTS (Nearest-to-Source), and FTG (Furthest-to-Go) protocols is unstable at rates View the MathML source for large enough values of C.
• The instability bound of the network subgraphs that are forbidden for stability is affected by the dynamic changes to the link capacities: we present improved instability bounds for all the directed subgraphs that were known to be forbidden for stability on networks running a certain greedy protocol.
