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

A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE

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

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

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks

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

Compositional Changes in Colostrum of Crossbred Dairy Cow

The research was conducted to examine the day-to-day variation in colostrum composition at the udder quarter level. For this purpose, a total of 3 Holstein Frisian crossbred cows were selected from Bangladesh Agricultural University Dairy Farm. Colostrum samples were collected both as mixed and separately from different teats. The concentration of major colostrum constituents (fat, protein, lactose, total solids, solids-not-fat, ash, pH, specific gravity) changed significantly (p≤0.05), the levels on day 4 were found similar to those of normal milk. The highest mean value of fat, protein, total solid, SNF, ash and specific gravity in colostrum was observed on 1st post-partum day as 6.02±0.70, 14.20±0.18, 23.88±1.25, 17.94±0.42, 1.03±0.05% and 1.05±0.00, respectively and later on, decreased as postpartum days advanced. Minimum average fat, protein, total solid, SNF, and ash content in colostrum was observed on 5th postpartum days as 3.75±0.11, 3.24±0.08, 12.00±0.20, 8.27±0.16% and 0.695±0.01, respectively. But lactose percent and pH showed an increasing trend from 1 to 5 postpartum days. Minimum average lactose and pH was observed on 1st and 5th postpartum days as 2.42±0.06%; 6.03±0.04% and 4.26±0.15; 6.30±0.04, respectively. The quality of colostrum produced by udder quarters was found significantly different (p<0.05). The rear quarters produced colostrum, which was significantly richer in fat, proteins, TS, pH compared to forequarters colostrum. The forequarters produced colostrum which was significantly richer in lactose, ash, SNF compared to forequarters colostrum. In conclusion, the results showed that colostrum composition was significantly changed up to 5 days post-partum

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