A 3D-collaborative wireless network: towards resilient communication for rescuing flood victims

Every year, floods result in huge damage and devastation both to lives and properties all over the world. Much of this devastation and its prolonged effects result from a lack of collaboration among the rescue agents as a consequence of the lack of reliable and resilient communication platform in the disrupted and damaged environments. In order to counteract this issue, this paper aims to propose a three-dimensional (3D)- collaborative wireless network utilizing air, water and ground based communication infrastructures to support rescue missions in flood-affected areas. Through simulated Search and Rescue(SAR) activities, the effectiveness of the proposed network model is validated and its superiority over the traditional SAR is demonstrated, particularly in the harsh flood environments. The model of the 3D-Collaborative wireless network is expected to significantly assist the rescuing teams in accomplishing their task more effectively in the corresponding disaster areas

Toward Wi-Fi Halow Signal Coverage Modeling in Collapsed Structures

With the emerging concept of Wi-Fi radio as sensors, we are witnessing more device-free sensing applications. But we observe that most of the existing works of these applications are meant for simple indoor layout and are not adequate for complex cases, e.g., collapsed structures. In this article, we explore the feasibility of Wi-Fi Halow signals for the collapsed scenario as it can boost rescue efforts. To achieve this, we aim at two prime objectives of this article. First, we model debris constituent of common collapsed scenario materials, such as concrete, brick, glass, and lumber by conducting a field survey of an earthquake-affected area. After that, we consider signal propagation models for better coverage in this debris model by employing two methods. The first method is an integrated TOPSIS and Shannon entropy-based on a bijective soft set, which provides us an approximation tool to select the best Wi-Fi Halow signal coverage in debris. The second method composes two modified wireless signal propagation models, which are transmitter-receiver (TR) and Wi-Fi radar, respectively. We perform extensive simulations and figure out that low power transmission using Wi-Fi radar can yield better coverage, which is also verified by the Shannon entropy method

Detecting movements of a target using face tracking in wireless sensor networks

Abstract—Target tracking is one of the key applications of wireless sensor networks (WSNs). Existing work mostly requires organizing groups of sensor nodes with measurements of a target’s movements or accurate distance measurements from the nodes to the target, and predicting those movements. These are, however, often difficult to accurately achieve in practice, especially in the case of unpredictable environments, sensor faults, etc. In this paper, we propose a new tracking framework, called FaceTrack, which employs the nodes of a spatial region surrounding a target, called a face. Instead of predicting the target location separately in a face, we estimate the target’s moving toward another face. We introduce an edge detection algorithm to generate each face further in such a way that the nodes can prepare ahead of the target’s moving, which greatly helps tracking the target in a timely fashion and recovering from special cases, e.g., sensor fault, loss of tracking. Also, we develop an optimal selection algorithm to select which sensors of faces to query and to forward the tracking data. Simulation results, compared with existing work, show that FaceTrack achieves better tracking accuracy and energy efficiency. We also validate its effectiveness via a proof-of-concept system of the Imote2 sensor platform. Index Terms—Wireless sensor networks, target tracking, sensor selection, edge detection, face tracking, fault tolerance Ç

Privacy-preserving distributed service recommendation based on locality-sensitive hashing

With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users’ service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems

Protected bidding against compromised information injection in IoT-based smart grid

The smart grid is regarded as one of the important application field of the Internet of Things (IoT) composed of embedded sensors, which sense and control the behavior of the energy world. IoT is attractive for features of grid catastrophe prevention and decrease of grid transmission line and reliable load fluctuation control. Automated Demand Response (ADR) in smart grids maintain demand-supply stability and in regulating customer side electric energy charges. An important goal of IoT-based demand-response using IoT is to enable a type of DR approach called automatic demand bidding (ADR-DB). However, compromised information board can be injected into during the DR process that influences the data privacy and security in the ADR-DB bidding process, while protecting privacy oriented consumer data is in the bidding process is must. In this work, we present a bidding approach that is secure and private for incentive-based ADR system. We use cryptography method instead of using any trusted third-party for the security and privacy. We show that proposed ADR bidding are computationally practical through simulations performed in three simulation environments

Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities

Dynamic Adaptation of Software-defined Networks for IoT Systems: A Search-based Approach

The concept of Internet of Things (IoT) has led to the development of many complex and critical systems such as smart emergency management systems. IoT-enabled applications typically depend on a communication network for transmitting large volumes of data in unpredictable and changing environments. These networks are prone to congestion when there is a burst in demand, e.g., as an emergency situation is unfolding, and therefore rely on configurable software-defined networks (SDN). In this paper, we propose a dynamic adaptive SDN configuration approach for IoT systems. The approach enables resolving congestion in real time while minimizing network utilization, data transmission delays and adaptation costs. Our approach builds on existing work in dynamic adaptive search-based software engineering (SBSE) to reconfigure an SDN while simultaneously ensuring multiple quality of service criteria. We evaluate our approach on an industrial national emergency management system, which is aimed at detecting disasters and emergencies, and facilitating recovery and rescue operations by providing first responders with a reliable communication infrastructure. Our results indicate that (1) our approach is able to efficiently and effectively adapt an SDN to dynamically resolve congestion, and (2) compared to two baseline data forwarding algorithms that are static and non-adaptive, our approach increases data transmission rate by a factor of at least 3 and decreases data loss by at least 70%