Hady S. AbdelSalam

BEES: BioinspirEd backbonE Selection in Wireless Sensor Networks

Sensor networks have their own distinguishing characteristics that set them apart from other types of networks. Several techniques have been proposed in the literature to address some of the fundamental problems faced by a sensor network design. Most of the proposed techniques attempt to solve one problem in isolation from the others; hence, protocol designers have to face the same common challenges again and again. This, in turn, has a direct impact on the complexity of the protocols and on energy consumption. Instead of using this approach, we propose BEES, a lightweight bioinspired backbone construction protocol, that can help mitigate many of the typical challenges in sensor networks by allowing the development of simpler network protocols. We show how BEES can help mitigate many of the typical challenges inherent to sensor networks including sensor localization, clustering, and data aggregation among others.

Towards Energy Efficient Change Management in a Cloud Computing Environment

The continuously increasing cost of managing IT systems has led many companies to outsource their commercial services to external hosting centers. Cloud computing has emerged as one of the enabling technologies that allow such external hosting efficiently. Like any IT environment, a Cloud Computing environment requires high level of maintenance to be able to provide services to its customers. Replacing defective items (hardware/software), applying security patches, or upgrading firmware are just a few examples of the typical maintenance procedures needed in such environments. While taking resources down for maintenance, applying efficient change management techniques is a key factor to the success of the cloud. As energy has become a precious resource, research has been conducted towards devising protocols that minimize energy consumption in IT systems. In this paper, we propose a pro-active energy efficient technique for change management in cloud computing environments. We formulate the management problem into an optimization problem that aims at minimizing the total energy consumption of the cloud. Our proposed approach is pro-active in the sense that it takes prior SLA (Service Level Agreement) requests into account while determining time slots in which changes should take place.

Toward Adaptive Sleep Schedules for Balancing Energy Consumption in Wireless Sensor Networks

In this work, we assume a geographic area populated by tiny sensors, each perhaps no larger than a dime. In order to save their energy, the sensors spend most of their lifetime in sleep mode and wake up for short periods of time to participate in various tasks supportive of the overall mission of the network. We assume that the tasks to be performed stipulate QoS parameters expressed in terms of the minimum number of sensors that need to monitor their sensing area. Since only awake sensors participate in tasks, the Effective Sensor Density (ESD), defined as the density of awake sensors, is an important network parameter that obviously depends on the sleep schedules adopted in the network. The first main contribution of this work is to provide a mathematical analysis of ESD from the perspective of the monitored events. We also provide design guidelines to determine deployment-time sensor density and an associated sleep schedule that probabilistically keeps the ESD at a level needed by QoS requirements. We also propose a fully distributed sleep schedule which adaptively adjusts the duty cycles of sensors within the same sensing area based on the relative difference in their remaining energy budget. The main advantage of the proposed adaptive scheme is to balance energy consumption among sensors, thus promoting the functional longevity of the sensor network without adversely affecting the ESD.

A Lightweight Skeleton Construction Algorithm for Self-Organizing Sensor Networks

Although, current technology enables an inexpensive massive production of sensors, it raises numerous challenges on the protocols needed to interact with these sensors efficiently. Several techniques have been proposed to address each of these challenges individually (i.e. localization, clustering, routing, aggregation ... etc). Instead of solving each of these problems individually facing the same common challenges with each problem, we propose to construct what we call a network skeleton that is constructed immediately after network deployment and provides a topology that makes the network more tractable. The skeleton provides sensors with coarse localization information that enables them to associate their sensory data with the geographic location in which the data was measured. Moreover, it promotes a geographic routing scheme that simplifies data communication across the network through skeleton sensors. By hypothetically tiling the deployment area using identical hexagons, the construction algorithm clusters sensors based on their locations into hexagons. Skeleton sensors are chosen to be the closest sensors to the centers of these hexagons. Simulation results show that the accuracy of the proposed protocol to establish the skeleton is sufficient to make the approach applicable for most WSN applications.

HexNet: Hexagon-based localization technique for wireless sensor networks

Developing efficient localization techniques to allow sensors to estimate their positions has been a hot research topic for long time. Sensory readings are almost meaningless if they are not associated with the locations in which these readings were taken. Getting location information through recording positions manually or through an expensive GPS chip are not valid options for sensor networks. We propose and evaluate, a new localization protocol for sensor networks. Although our technique is presented using only a single anchor, it can be extended easily to benefit from the existence of several anchors. The proposed technique starts by hypothetically tiling the deployment area around the anchor node using identical hexagons. After that, the closest sensor node to the center of each of these hypothetical hexagons is determined, we refer to these nodes as backbone sensors. The positions of hexagon centers hence the positions of backbone sensors are estimated geometrically. After that, backbone sensors are treated as beacons and the positions of all non-backbone sensors are estimated using the centroid approach. Simulation results under noise-free and noisy conditions show that the proposed protocol achieves a localization accuracy that makes it useful for most WSN applications.

Towards Enhanced RSSI-Based Distance Measurements and Localization in WSNs

Despite its inherent inaccuracy, RSSI has been used as the standard tool for distance measurements in the majority of range-based localization protocols available in the literature. In this paper, we propose a technique to enhance the accuracy of RSSI-based distance measurements using calibration. Our technique depends on the existence of location-aware anchor nodes that are distributed across the deployment area. Initially and through exchanging messages between anchors, the deployment area is divided into disjoint set of Delaunay triangles. Through the power of received signals, anchors estimate a set of calibration records(CR) and distribute them to sensors in their neighborhood. Each triangle edge connects a pair of anchors and is associated with a single calibration record. Each record contains calibration parameters for sensors in the neighborhood of the record anchors. Based on the power of received messages from different anchors, sensors can determine the calibration record they should use for their RSSI-based distance measurements.

A 3d-localization and terrain modeling technique for wireless sensor networks

Although sensor networks are usually deployed in complex 3D terrains, the majority of localization techniques proposed in the literature are designed assuming 2D deployments. Furthermore, in general it is not easy to extend these techniques to 3D deployments. With minimal communication and computation overhead, we present a 3D-localization technique that comes with a terrain modeling capability that makes the technique more interesting. Sensors are localized in two steps. First, we determine the horizontal plane within which the sensor lies. Then, we project the nearest three anchors to the sensor onto the horizontal plane determined in the first step. Finally, we use RSSI-based distance measurements and trilateration techniques to fully localize the sensor. After localization, we use Delaunay triangulation, to build a mesh that models the terrain of the deployment area. In addition to the terrain modeling capability, the proposed technique has several attractive features, such as scalability and independence from the underlying network topology. Simulation results demonstrate that the proposed approach can achieve high accuracy in sensor localization and terrain modeling under simple and moderate 3D deployments. Intuitively, the accuracy of the modeled terrain increases with network density as in such cases there will be more vertices available to construct a more accurate mesh.

Toward Efficient Task Management in Wireless Sensor Networks

In numerous applications of wireless sensor networks (WSN), the reliability of the data collected by sensors is cast as specific QoS requirements expressed in terms of the minimum number of sensors needed to perform various tasks. Designing a long-lived sensor network with reliable performance has always been challenging due to the modest nonrenewable energy budget of individual sensors. In such a context, energy-unaware task management protocols may result in uneven expenditure of sensor energy by assigning uneven workloads to sensors. This, in turn, often translates into reduced sensor density around those heavily loaded sensors and may, eventually, lead to the creation of energy holes that partition the network into disconnected islands. To avoid these problems and to promote network longevity, we propose two energy-aware task management protocols: our first protocol is centralized, while the second one is fully distributed. The proposed protocols assign tasks to sensors based on their remaining energy so that energy expenditure among neighboring sensors is almost even. We compare the reliable lifetime of the network achieved by assigning tasks to sensors using the proposed protocols against optimal task assignment and also against energy-unaware protocols. Extensive simulation results have revealed that the performance of the proposed protocols is very close to that of the optimal task assignment. Furthermore, our simulation has shown that the proposed protocols can increase the functional longevity of the network by about 16 percent.

Energy efficient workforce selection in special-purpose wireless sensor networks

Sensors are typically powered by non-renewable energy sources (e.g. batteries). This fact makes sensors energy very precious resource that must be saved and used wisely. Many protocols have been proposed to optimize energy consumption in different areas of WSNs (e.g. MAC, routing, data aggregation, etc). However, literature has paid less attention to energy efficient workforce management, namely the way sensors are being recruited. In this work, we show that tasking sensors improperly may result in severely uneven consumption of sensors energy. This can negatively impact network performance through reducing network density, creating energy holes or reducing network lifetime. For the limited communication capabilities of sensors, communication in WSNs deployed in large areas is multihop by nature. However for some special WSN applications (e.g firefighting and handicapped assistance) where the central Aggregating Node (AN) (mounted on the firefighter helmet or the handicapped stick) has low mobility, the monitored area of interest is temporarily limited to a small disk that is centered at the AN. Sensors that reside on that moving disk and the AN form a dynamic WSN where a single hop communication model is appropriate and beneficial. For such scenarios, we propose and evaluate a new protocol that provides an energy efficient workforce management for single and multihop WSNs. Analytical and simulation results demonstrate that the proposed approach significantly increases network lifetime by evenly consuming sensors energy. We also propose an algorithm to extend the single hop version of the protocol to a multihop environment.

Tiling-Based Localization Scheme for Sensor Networks Using a Single Beacon

We propose and evaluate a new localization protocol for sensor networks. Contrary to most current localization schemes, this protocol relies on the existence of only a single beacon node. The main idea is to hypothetically tile the deployment area with identical equilateral geometric shapes (e.g. hexagons, triangles... etc). Tiling should be such that the positions of the centers of these shapes can be easily computed relative to the position of the beacon node. After that, the set of nodes that are as close as possible to the centers of these shapes is determined. We refer to these nodes as central nodes. Basically, the positions of these central nodes can be approximated by the positions of the centers of the geometric shapes which can be calculated mathematically. These central nodes can be treated as beacons and the positions of all non-central nodes can be estimated using any range-free protocol (e.g. the centroid). Simulation results show that the accuracy of the proposed protocol is comparable to the accuracy of current localization protocols especially near the beacon node. In addition to this, the achieved localization accuracy increases significantly as the network density increases.