Locally Self-Adjusting Skip Graphs

We present a distributed self-adjusting algorithm for skip graphs that minimizes the average routing costs between arbitrary communication pairs by performing topological adaptation to the communication pattern. Our algorithm is fully decentralized, conforms to the $\mathcal{CONGEST}$ model (i.e. uses $O(\log n)$ bit messages), and requires $O(\log n)$ bits of memory for each node, where $n$ is the total number of nodes. Upon each communication request, our algorithm first establishes communication by using the standard skip graph routing, and then locally and partially reconstructs the skip graph topology to perform topological adaptation. We propose a computational model for such algorithms, as well as a yardstick (working set property) to evaluate them. Our working set property can also be used to evaluate self-adjusting algorithms for other graph classes where multiple tree-like subgraphs overlap (e.g. hypercube networks). We derive a lower bound of the amortized routing cost for any algorithm that follows our model and serves an unknown sequence of communication requests. We show that the routing cost of our algorithm is at most a constant factor more than the amortized routing cost of any algorithm conforming to our computational model. We also show that the expected transformation cost for our algorithm is at most a logarithmic factor more than the amortized routing cost of any algorithm conforming to our computational model

Distributed Traffic Signal Control for Maximum Network Throughput

We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities

An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each node’s energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal agent-based decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured

Energy-aware routing in multiple domains software defined networks

The growing energy consumption of communication networks has attracted the attention of the networking researchers in the last decade. In this context, the new architecture of Software-Defined Networks (SDN) allows a flexible programmability, suitable for the power-consumption optimization problem. In this paper we address the issue of designing a novel distributed routing algorithm that optimizes the power consumption in large scale SDN with multiple domains. The solution proposed, called DEAR (Distributed Energy- Aware Routing), tackles the problem of minimizing the number of links that can be used to satisfy a given data traffic demand under performance constraints such as control traffic delay and link utilization. To this end, we present a complete formulation of the optimization problem that considers routing requirements for control and data plane communications. Simulation results confirm that the proposed solution enables the achievement of significant energy savings.Peer ReviewedPostprint (published version

Towards green communication in wireless sensor network: GA enabled distributed zone approach

[EN] Green communication in wireless sensor networks (WSNs) has witnessed significant attention due to the growing significance of sensor enabled smart environments. Energy optimization and communication optimization are two major themes of investigation for green communication. Due to the growing sensor density in smart environment, intelligently finding shortest path for green communication has been proven an NP-complete problem. Literature in green communication majorly focuses towards finding centralized optimal path solution. These centralized optimal-path finding solutions were suitable for application specific traditional WSNs environments. The cutting edge sensor enabled smart environments supporting heterogender applications require distributed optimal path finding solutions for green communication. In this context, this paper proposes a genetic algorithm enabled distributed zone approach for green communication. Specifically, instead of searching the optimal path solution in the whole network, the proposed algorithm identifies path in a small search space called distributed forward zone. The concept of forward zone enhances the searching convergence speed and reduces the computation centric communication cost. To encode the distributed routing solutions, variable length chromosomes are considered focusing on the target distributed area. The genetic algorithm enabled distributed zone approach prevents all the possibilities of forming the infeasible chromosomes. Crossover and truncation selection together generate a distributed path finding solution. To validate the experimental results with analytical results, various mathematical models for connectivity probability, expected end-to-end delay, expected energy consumption, and expected computational cost have been derived. The simulation results show that the proposed approach gives the high-quality solutions in comparison to the state-of-the-art techniques including Dijkstra's algorithm, compass routing, most forward within radius, Ahn-Ramakrishna's algorithm and reliable routing with distributed learning automaton (RRDLA).Kumar, S.; Kumar, V.; Kaiwartya, O.; Dohare, U.; Kumar, N.; Lloret, J. (2019). Towards green communication in wireless sensor network: GA enabled distributed zone approach. Ad Hoc Networks. 93:1-17. https://doi.org/10.1016/j.adhoc.2019.1019031179

The Four Principles of Geographic Routing

Geographic routing consists in using the position information of nodes to assist in the routing process, and has been a widely studied subject in sensor networks. One of the outstanding challenges facing geographic routing has been its applicability. Authors either make some broad assumptions on an idealized version of wireless networks which are often unverifiable, or they use costly methods to planarize the communication graph. The overarching questions that drive us are the following. When, and how should we use geographic routing? Is there a criterion to tell whether a communication network is fit for geographic routing? When exactly does geographic routing make sense? In this paper we formulate the four principles that define geographic routing and explore their topological consequences. Given a localized communication network, we then define and compute its geographic eccentricity, which measures its fitness for geographic routing. Finally we propose a distributed algorithm that either enables geographic routing on the network or proves that its geographic eccentricity is too high.Comment: This manuscript on geographic routing incoporates team feedback and expanded experiment