Basilis Mamalis

A Rendezvous-Based Approach Enabling Energy-Efficient Sensory Data Collection with Mobile Sinks

A large class of Wireless Sensor Networks (WSN) applications involve a set of isolated urban areas (e.g., urban parks or building blocks) covered by sensor nodes (SNs) monitoring environmental parameters. Mobile sinks (MSs) mounted upon urban vehicles with fixed trajectories (e.g., buses) provide the ideal infrastructure to effectively retrieve sensory data from such isolated WSN fields. Existing approaches involve either single-hop transfer of data from SNs that lie within the MSs range or heavy involvement of network periphery nodes in data retrieval, processing, buffering, and delivering tasks. These nodes run the risk of rapid energy exhaustion resulting in loss of network connectivity and decreased network lifetime. Our proposed protocol aims at minimizing the overall network overhead and energy expenditure associated with the multihop data retrieval process while also ensuring balanced energy consumption among SNs and prolonged network lifetime. This is achieved through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). CHs perform data filtering upon raw data exploiting potential spatial-temporal data redundancy and forward the filtered information to appropriate end nodes with sufficient residual energy, located in proximity to the MSs trajectory. Simulation results confirm the effectiveness of our approach against as well as its performance gain over alternative methods.

Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach

Organization, scalability and routing have been identified as key problems hindering viability and commercial success of mobile ad hoc networks. Clustering of mobile nodes among separate domains has been proposed as an efficient approach to address those issues. In this work, we introduce an efficient distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution mainly addresses cluster stability, manageability and energy efficiency issues. Also, unlike existing active clustering methods, our algorithm relieves the network from the unnecessary burden of control messages broadcasting, especially for relatively static network topologies. This is achieved through adapting broadcast period according to mobile nodes mobility pattern. The efficiency, scalability and competence of our algorithm against alternative approaches have been demonstrated through simulation results.

Lowest-ID with adaptive ID reassignment: a novel mobile ad-hoc networks clustering algorithm

Clustering is a promising approach for building hierarchies and simplifying the routing process in mobile ad-hoc network environments. The main objective of clustering is to identify suitable node representatives, i.e. cluster heads (CHs), to store routing and topology information and maximize clusters stability. Traditional clustering algorithms suggest CH election exclusively based on node IDs or location information and involve frequent broadcasting of control packets, even when network topology remains unchanged. More recent works take into account additional metrics (such as energy and mobility) and optimize initial clustering. However, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon reach battery exhaustion. Herein, we introduce an efficient distributed clustering algorithm that uses both mobility and energy metrics to provide stable cluster formations. CHs are initially elected based on the time and cost-efficient lowest-ID method. During clustering maintenance phase though, node IDs are re-assigned according to nodes mobility and energy status, ensuring that nodes with low-mobility and sufficient energy supply are assigned low IDs and, hence, are elected as CHs. Our algorithm also reduces control traffic volume since broadcast period is adjusted according to the nodes mobility pattern: we employ infrequent broadcasting for relative static network topologies, and increase broadcast frequency for highly mobile network configurations. Simulation results verify that energy consumption is uniformly distributed among network nodes and that signaling overhead is significantly decreased.

ABP: a low-cost, energy-efficient clustering algorithm for relatively static and quasi-static MANETs

Clustering techniques have been proposed to construct hierarchies of nodes inside Mobile Ad Hoc Networks (MANETs) thereby increasing their scalability and manageability and reducing the amount of maintained routing information. Herein, we introduce a distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution addresses cluster stability, manageability and energy efficiency issues. Unlike existing active clustering methods, our algorithm relieves the network from the unnecessary burden of control messages broadcasting, especially for relatively static and quasi-static network topologies. The efficiency, scalability and competence of our algorithm against alternative approaches have been demonstrated through simulation results.

Parallel text retrieval on a high performance supercomputer using the Vector Space Model

This paperl discusses the efi-iciency of a parallel text retrieval system that is based on the Vector Space Model. Specifically, we describe a general parallel retrieval algorithm for use with this model, the application of the algorithm in the FIRE system [I], and its implementation on the high performance GCe131512 Parsytec parallel machine [2]. The use of this machine’s t we-dimensional grid of processors provides an efficient baais for the virtual tree that lies at the heart of our retrieval algorithm. Analytical and experimental evidence is presented to demonstrate the efficiency of the algorithm.

Watershed-based clustering for energy efficient data gathering in wireless sensor networks with mobile collector

This paper presents a clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks (WSNs). The main insight for the cluster creation method is drawn from image processing field and namely from the watershed transformation which is widely used for image segmentation. The proposed algorithm creates multi-hop clusters whose clusterheads (CHs) as well as all cluster members near the CHs have high energy reserves. As these are exactly the nodes most burdened with relaying of data from other cluster members, the higher levels of available energy at these nodes prolong the network lifetime eventually. After cluster creation, a MS periodically visits each CH and collects the data from cluster members already gathered at the CH. Simulation results show the higher performance of the proposed scheme in comparison to other competent approaches from the literature.

Mobile Sinks for Information Retrieval from Cluster-Based WSN Islands

Mobile sinks (MS) mounted upon urban vehicles with fixed trajectories (e.g. buses) provide the ideal infrastructure to effectively retrieve sensory data from isolated Wireless Sensor Network (WSN) fields. Existing approaches involve either single-hop transfer of data from sensors that lie within the MSs range or heavy involvement of network periphery nodes in data retrieval, processing, buffering and delivering tasks. These nodes run the risk of rapid energy exhaustion resulting in loss of network connectivity. Our proposed protocol aims at minimizing the overall network overhead and energy expenditure associated with the multi-hop data retrieval process while also ensuring balanced energy consumption among network nodes and prolonged network lifetime. This is achieved through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). CHs perform data filtering upon raw data exploiting potential spatial-temporal data redundancy and forward the filtered information to appropriate end nodes.

LIDAR: a protocol for stable and energy-efficient clustering of ad-hoc multihop networks

Abstract Clustering has been proposed as a promising method for simplifying the routing process in mobile ad hoc networks (MANETs). The main objective in clustering is to identify suitable node representatives, i.e. cluster heads (CHs) to store routing and topology information; CHs should be elected so as to maximize clusters stability, that is to prevent frequent cluster re-structuring. Since CHs are engaged on packet forwarding they are prone to rapidly drop their energy supplies, hence, another important objective of clustering is to prevent such node failures. Recently proposed clustering algorithms either suggest CH election based on node IDs (nodes with locally lowest ID value become CHs) or take into account additional metrics (such as energy and mobility) and optimize initial clustering. Yet, the former method is biased against nodes with low IDs (which are likely to serve as CHs for long periods and therefore run the risk of rapid battery exhaustion). Similarly, in the latter method, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon suffer from energy drainage. Herein, we propose LIDAR, a novel clustering method which represents a major improvement over alternative clustering algorithms: node IDs are periodically re-assigned so that nodes with low mobility rate and high energy capacity are assigned low ID values and, therefore, are likely to serve as CHs. Therefore, LIDAR achieves stable cluster formations and balanced distribution of energy consumption over mobile nodes. Our protocol also greatly reduces control traffic volume of existing algorithms during clustering maintenance phase, while not risking the energy availability of CHs. Simulation results demonstrate the efficiency, scalability and stability of our protocol against alternative approaches.

An image processing inspired mobile sink solution for energy efficient data gathering in wireless sensor networks

This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main insight for the clustering algorithm is drawn from image processing field and namely from the watershed transform, widely used for image segmentation. The proposed algorithm creates multi-hop clusters whose cluster heads (CHs) as well as cluster members near the CHs have high energy reserves. Specifically, the energy of the sensors in a cluster increases progressively as getting closer to the CH. As the nodes closer to the CH are most burdened with relaying of data from other cluster members, the higher levels of available energy at these nodes prolong the network lifetime eventually. After cluster formation, a MS periodically visits each CH and collects the data from cluster members already gathered at the CH. Simulation results show the higher performance of the proposed scheme in comparison to other competent approaches in the literature.

Efficient parallel Text Retrieval techniques on Bulk Synchronous Parallel (BSP)/Coarse Grained Multicomputers (CGM)

In this paper, we present efficient, scalable, and portable parallel algorithms for the off-line clustering, the on-line retrieval and the update phases of the Text Retrieval (TR) problem based on the vector space model and using clustering to organize and handle a dynamic document collection. The algorithms are running on the Coarse-Grained Multicomputer (CGM) and/or the Bulk Synchronous Parallel (BSP) model which are two models that capture within a few parameters the characteristics of the parallel machine. To the best of our knowledge, our parallel retrieval algorithms are the first ones analyzed under these specific parallel models. For all the phases of the proposed algorithms, we analytically determine the relevant communication and computation cost thereby formally proving the efficiency of the proposed solutions. In addition, we prove that our technique for the on-line retrieval phase performs very well in comparison to other possible alternatives in the typical case of a multiuser information retrieval (IR) system where a number of user queries are concurrently submitted to an IR system. Finally, we discuss external memory issues and show how our techniques can be adapted to the case when processors have limited main memory but sufficient disk capacity for holding their local data.