Waleed A. Youssef

Positioning of Base Stations in Wireless Sensor Networks

Wireless sensor networks (WSN) have attracted much attention in recent years due to their potential use in many applications such as border protection and combat field surveillance. Given the criticality of such applications, maintaining a dependable operation of the network is a fundamental objective. However, the resource-constrained nature of sensor nodes and the ad hoc formation of the network, often coupled with an unattended deployment, pose non-conventional challenges and motivate the need for special techniques for dependable design and management of WSN. In this article, we highlight the potential of careful positioning of the base station (BS), which acts as a sink node for the collected data, as a viable means for increasing the dependability of WSN. We categorize published work on optimal positioning of BS in WSN. Referring to such work as static positioning, we further introduce dynamic schemes that reposition the BS during the network operation. We show that dynamic BS positioning can be very effective in optimizing the network functional and non-functional performance objectives and in coping with dynamic changes in the environment and available network resources

An Intelligent Safety-Aware Gateway Relocation Scheme for Wireless Sensor Networks

Recently, wireless sensor networks (WSN) have received enormous attentions due to their potential use in many applications. They can be used to enrich our understanding of natural events, such as earthquakes and volcanoes, and to increase the efficiency of surveillance operations in secure installation, border control and military reconnaissance. Sensors are placed in harsh environments to collect and deliver data to a central node, called the gateway. The gateway analyzes the received data and decides on appropriate actions. Therefore, protecting the gateway is critical for ensuring the robustness of WSN. Since the location of the gateway significantly affects the efficiency of the network operation, many research have been conducted for the gateway placement problem. However, most of the proposed solutions are geared for boosting network-related performance metrics, such as throughput and energy consumption. We argue that relocating without taking safety concerns into consideration may cause the gateway to move dangerously close to one or multiple serious events in the environment. In this paper, we present GRENN, a new algorithm for gateway relocation in wireless sensor networks considering both the network performance and the gateway safety. Our experimental validation has demonstrated the effectiveness of GRENN in protecting the gateway while keeping the performance at an acceptable level.

Optimized asset planning for minimizing latency in wireless sensor networks

Wireless Sensor Networks (WSNs) has been attracting lots of interest in recent years. In such networks sensors data are collected over multi-hop routes at one or multiple base-stations (gateway nodes) for processing. In many WSN applications such as disaster management and combat field surveillance, rapid response to detected events is necessary and thus data latency should be minimal. Given the sensor’s energy and radio range constraints, direct communication with the gateway is inefficient and often infeasible for most deployed sensors. An intuitive approach to limit data latency is to increase the population of gateways and place them in the vicinity of sensors. However, gateway nodes are typically costly and thus it is desired to limit their count. Therefore, there is a need to balance between such conflicting requirements. In this paper, we pursue an integrated approach to asset planning in WSNs so that the data latency is minimized. The goal is to determine the least number of gateways and identify where to place them in the network in order to achieve a certain delay bound on data delivery. We formulate an optimization model for the asset planning problem and present effective algorithms for solving it. The proposed solution scheme employs contemporary search heuristics such as k-means and genetic algorithms. Validation results confirm the effectiveness of our approach in achieving the desired design goals.

A cognitive scheme for gateway protection in wireless sensor network

Abstract In wireless sensor networks, sensor readings are gathered at a gateway for processing and forwarding to a remote command center. The potential closeness of the gateway to dangerous events, e.g. fires, exposes it to damage and thus risks making the network dysfunctional. Therefore, protecting the gateway by repositioning it away from safety-hazardous spots is critical for the operation of the network. However, moving the gateway too far from the sensors that report on active events would have negative effect on the network performance, e.g. throughput and energy consumption. Therefore, balancing the gateway safety and network performance goals will be necessary. In this paper, we present GRISP, a novel Gateway Relocation algorithm for Improved Safety and Performance. GRISP employs an evolutionary neural network model to assess the safety of the gateway at the various locations. The model is then used to direct the search in an area of interest for a safer position that would enhance or at least maintain an acceptable level of network performance. In addition, GRISP guides the gateway during the move by finding safe paths leading to the new location. Our experimental validation results demonstrate the effectiveness of GRISP.

Safety- and QoS-aware management of heterogeneous sensor networks

Recently, we have proposed ANSWER: AutoNomouS Wireless sEnsor netwoRk as a service platform whose mission is to provide dependable information services to in-situ mobile users while satisfying their quality-of-service (QoS) requirements. Alongside with the stationary tiny sensors, the network employs more powerful mobile devices referred to as aggregation and forwarding nodes (AFNs). ANSWER exploits AFN mobility to support QoS requirements. However, as an AFN moves closer to an event (e.g., a hazardous spill), it may be at risk (e.g., due to potential damage). In this paper, we present a quantitative analysis of the interplay and balance between QoS support and asset safety. We propose a new scheme, called SAFER (for SAFEty-aware Relocation), which pursues relocation of the AFN in order to boost network performance without unnecessarily compromising AFN safety. SAFER uses historical data on detected events and employs an evolutionary neural network to assess the risk involved and predict good quality new position(s).

Improving gateway safety in wireless sensor networks using cognitive techniques

In this paper, we present an innovative approach for gateway relocation in Wireless Sensor Networks (WSNs). We employ existing well-known artificial intelligence techniques to make smart gateway relocation based on many input factors from the environment. The gateway placement problem has been extensively researched. However, most of the proposed solutions are geared for boosting network-related performance metrics, such as throughput and energy consumption. The proposed approach for safe gateway relocation, named G-Safe, emphasises both increased protection of the gateway and improvements in overall network performance. We argue that relocating without taking safety concerns into consideration may cause the gateway to move dangerously close to one or multiple serious events in the environment. G-Safe utilises artificial neural networks to select the safest location accessible to the gateway. Experimental validation of G-Safe was conducted in two different environment setups; one that emphasises improvements in network longevity and the other strives to enhance the timeliness of the collected sensor readings. The validation results have confirmed the effectiveness of G-Safe in protecting the gateway while keeping the performance at an acceptable level.

Intelligent Estimation of Gateways Count for Reduced Data Latency in Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently gained increased attention due to their potential use in numerous civil and military applications. In many of these applications, resource-constrained sensors are deployed to probe their surroundings and report their findings to one or multiple gateway nodes. In large networks, many sensors get involved in data relaying making the data latency unacceptably high. Since the number of deployed gateways and their locations impact the operation of the network, one of the option for reducing the data collection delay is to employ many gateways and appropriately place them in close proximity to data sources. However, increasing the gateway count imposes high cost for the additional hardware resources and their deployment means, thus a tradeoff would be unavoidable. In this paper, we introduce a novel approach for estimating the least number of gateways required for meeting some upper bound on data latency. In addition, we present a heuristic for finding best locations for these gateways. Validation results confirm the effectiveness of the proposed approach.

Intelligent Discovery of Safe Paths in Wireless Sensor Network

In wireless sensor networks, sensor readings are gathered at a gateway for processing and forwarding to a remote commend center. The closeness of the gateway to potentially dangerous events, e.g. fires, exposes it to damage and thus risks making the network dysfunctional. Recently, we have introduced SAFER (safety-aware relocation); a novel approach for protecting the gateway. SAFER strives to relocate the gateway to a safer location while maintaining acceptable network performance. In this paper, we propose to extend SAFER by introducing a new scheme for guiding the gateway during relocation, i.e. finding a safety and operation aware path (SOAP). The SOAP employs evolutionary neural networks to estimate the risk that the gateway may be running at various candidate locations and identifies a safe spot for the gateway. SOAP helps the gateway in safely reaching its final destination and maintaining uninterruptible data delivery. Simulation results demonstrate the effectiveness of our proposed approach

Safe Quality of Service Aware Management of Heterogeneous Sensor Networks

Wireless networked sensors are envisioned to play a vital role in the emerging pervasive service platforms that will instrument a wide range of next generation civil and military applications. Recently, we have proposed ANSWER: AutoNomouS Wireless sEnsor netwoRk as a service platform whose mission is to provide dependable information services to in-situ mobile users while satisfying their quality-of-service (QoS) requirements. Alongside with the stationary tiny sensors, the network employs more powerful mobile devices referred to as aggregation and forwarding nodes (AFNs). ANSWER exploits AFN mobility to support QoS requirements. However, as an AFN moves closer to an event (for example, a hazardous spill), it may be at risk (for example, due to potential damage). In this paper, we present a quantitative analysis of the interplay and balance between QoS support and asset safety. We propose a new scheme, called SAFER (for SAFEty-aware Relocation), which pursues relocation of the AFN in order to boost network performance without unnecessarily compromising AFN safety. For the relocation process to take place, SAFER uses historical data on detected events and employs an evolutionary neural network to assess the risk involved and predict good quality new position(s). Experimental results demonstrate the effectiveness of SAFER.