Wireless Communications

Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

/pdf/heed-a-hybrid-energy-efficient-distributed-clustering-4c3cjgsfv9.pdf

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

An Introduction to MultiAgent Systems.

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

/pdf/energy-conservation-in-wireless-sensor-networks-a-survey-1bl7d2obnm.pdf

Energy conservation in wireless sensor networks: A survey

http://cpham.perso.univ-pau.fr/ENSEIGNEMENT/PAU-UPPA/RESA-M2/Clustering-abasi.pdf

A survey on clustering algorithms for wireless sensor networks

In this paper, we propose an on-demand distributed clustering algorithm for multi-hop packet radio networks. These types of networks, also known as i>ad hoc networks, are dynamic in nature due to the mobility of nodes. The association and dissociation of nodes to and from i>clusters perturb the stability of the network topology, and hence a reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The i>clusterheads, form a i>dominant set in the network, determine the topology and its stability. The proposed weight-based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. The non-periodic procedure for clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. The clusterheads, operating in “dual" power mode, connects the clusters which help in routing messages from a node to any other node. We observe a trade-off between the uniformity of the load handled by the clusterheads and the connectivity of the network. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, i>reaffiliation frequency, and dominant set updates. Results show that our algorithm performs better than existing ones and is also tunable to different kinds of network conditions.

/pdf/wca-a-weighted-clustering-algorithm-for-mobile-ad-hoc-4mxdzu4mqn.pdf

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

https://www.eecs.ucf.edu/~turgut/Research/Publications/Download/Boukerche-2011-ComputerNetworks.pdf

Survey Paper: Routing protocols in ad hoc networks: A survey

We consider a multi-cluster, multi-hop packet radio network architecture for wireless systems which can dynamically adapt itself with the changing network configurations. Due to the dynamic nature of the mobile nodes, their association and dissociation to and from clusters perturb the stability of the system, and hence a reconfiguration of the system is unavoidable. At the same time it is vital to keep the topology stable as long as possible. The clusterheads, which form a dominant set in the network, decide the topology and are responsible for its stability. In this paper, we propose a weighted clustering algorithm (WCA) which takes into consideration the ideal degree, transmission power, mobility and battery power of a mobile node. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol, Our clusterhead election procedure is not periodic as in earlier research, but adapts based on the dynamism of the nodes. This on-demand execution of WCA aims to maintain the stability of the network, thus lowering the computation and communication costs associated with it. Simulation experiments are conducted to evaluate the performance of WCA in terms of the number of clusterheads, reaffiliation frequency and dominant set updates, Results show that the WCA performs better than the existing algorithms and is also tunable to different types of ad hoc networks.

/pdf/an-on-demand-weighted-clustering-algorithm-wca-for-ad-hoc-4ui6c7xx73.pdf

An on-demand weighted clustering algorithm (WCA) for ad hoc networks

Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here, since taxi requests in the future are correlated with information about actions that happened in the past. For example, someone who requests a taxi to a shopping center, may also request a taxi to return home after few hours. We use one of the best sequence learning methods, long short term memory that has a gating mechanism to store the relevant information for future use. We evaluate our method on a data set of taxi requests in New York City by dividing the city into small areas and predicting the demand in each area. We show that this approach outperforms other prediction methods, such as feed-forward neural networks. In addition, we show how adding other relevant information, such as weather, time, and drop-offs affects the results.

Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks

We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks In particular, we optimize our recently proposed weighted clustering algorithm (WCA) The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the clusterheads and the members thereof, as obtained from the original WCA The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control (MAC) protocol Consequently, it results in the minimum number of clusters and hence clusterheads Simulation results exhibit improved performance of the optimized WCA than the original WCA Moreover, the loads among clusters are more evenly balanced by a factor of ten

/pdf/optimizing-clustering-algorithm-in-mobile-ad-hoc-networks-3uat41r1mn.pdf

Damla Turgut

Papers

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

Survey Paper: Routing protocols in ad hoc networks: A survey

An on-demand weighted clustering algorithm (WCA) for ad hoc networks

Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks

Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach