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J. Sens. Actuator Netw., Volume 7, Issue 3 (September 2018)

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Open AccessArticle Trajectory Assisted Municipal Agent Mobility: A Sensor-Driven Smart Waste Management System
J. Sens. Actuator Netw. 2018, 7(3), 29; https://doi.org/10.3390/jsan7030029 (registering DOI)
Received: 12 June 2018 / Revised: 12 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Abstract
Ubiquity, heterogeneity and dense deployment of sensors have yielded the Internet of Things (IoT) concept, which is an integral component of various smart spaces including smart cities. Applications and services in a smart city ecosystem aim at minimizing the cost and maximizing the
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Ubiquity, heterogeneity and dense deployment of sensors have yielded the Internet of Things (IoT) concept, which is an integral component of various smart spaces including smart cities. Applications and services in a smart city ecosystem aim at minimizing the cost and maximizing the quality of living. Among these services, waste management is a unique service that covers both aspects. To this end, in this paper, we propose a WSN-driven system for smart waste management in urban areas. In our proposed framework, the waste bins are equipped with sensors that continuously monitor the waste level and trigger alarms that are wirelessly communicated to a cloud platform to actuate the municipal agents, i.e., waste collection trucks. We formulate an Integer Linear Programming (ILP) model to find the best set of trajectory-truck with the objectives of minimum cost or minimum delay. In order for the trajectory assistance to work in real time, we propose three heuristics, one of which is a greedy one. Through simulations, we show that the ILP formulation can provide a baseline reference to the heuristics, whereas the non-greedy heuristics can significantly outperform the greedy approach regarding cost and delay under moderate waste accumulation scenarios. Full article
(This article belongs to the Special Issue Wireless Sensor and Actuator Networks for Smart Cities)
Open AccessReview Security Vulnerabilities in Bluetooth Technology as Used in IoT
J. Sens. Actuator Netw. 2018, 7(3), 28; https://doi.org/10.3390/jsan7030028
Received: 24 April 2018 / Revised: 15 June 2018 / Accepted: 12 July 2018 / Published: 19 July 2018
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Abstract
Bluetooth technology is a key component of wireless communications. It provides a low-energy and low-cost solution for short-range radio transmissions. Bluetooth, more specifically Bluetooth Low Energy (BLE) has become the predominant technology for connecting IoT (Internet of Things). It can be found in
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Bluetooth technology is a key component of wireless communications. It provides a low-energy and low-cost solution for short-range radio transmissions. Bluetooth, more specifically Bluetooth Low Energy (BLE) has become the predominant technology for connecting IoT (Internet of Things). It can be found in cell phones, headsets, speakers, printers, keyboards, automobiles, children’s toys, and medical devices, as well as many other devices. The technology can also be found in automated smart homes, to provide monitors and controls for lights, thermostats, door locks, appliances, security systems, and cameras. Bluetooth offers convenience and ease of use, but it lacks a centralized security infrastructure. As a result, it has serious security vulnerabilities, and the need for awareness of the security risks are increasing as the technology becomes more widespread. This paper presents an overview of Bluetooth technology in IoT including its security, vulnerabilities, threats, and risk mitigation solutions, as well as real-life examples of exploits. Our study highlights the importance of understanding attack risks and mitigation techniques involved with using Bluetooth technology on our devices. Real-life examples of recent Bluetooth exploits are presented. Several recommended security measures are discussed to secure Bluetooth communication. Full article
(This article belongs to the Special Issue Sensors and Actuators: Security Threats and Countermeasures)
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Open AccessArticle Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
J. Sens. Actuator Netw. 2018, 7(3), 27; https://doi.org/10.3390/jsan7030027
Received: 27 April 2018 / Revised: 6 July 2018 / Accepted: 10 July 2018 / Published: 12 July 2018
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Abstract
Long-Term Evolution (LTE) and its improvement, Long-Term Evolution-Advanced (LTE-A), are attractive choices for Machine-to-Machine (M2M) communication due to their ubiquitous coverage and high bandwidth. However, the focus of LTE design was high performance connection-based communications between human-operated devices (also known as human-to-human, or
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Long-Term Evolution (LTE) and its improvement, Long-Term Evolution-Advanced (LTE-A), are attractive choices for Machine-to-Machine (M2M) communication due to their ubiquitous coverage and high bandwidth. However, the focus of LTE design was high performance connection-based communications between human-operated devices (also known as human-to-human, or H2H traffic), which was initially established over the Physical Random Access Channel (PRACH). On the other hand, M2M traffic is mostly based on contention-based transmission of short messages and does not need connection establishment. As a result, M2M traffic transmitted over LTE PRACH has to use the inefficient four-way handshake and compete for resources with H2H traffic. When a large number of M2M devices attempts to access the PRACH, an outage condition may occur; furthermore, traffic prioritization is regulated only through age-based power ramping, which drives the network even faster towards the outage condition. In this article, we describe an overlay network that allows a massive number of M2M devices to coexist with H2H traffic and access the network without going through the full LTE handshake. The overlay network is patterned after IEEE 802.15.6 to support multiple priority classes of M2M traffic. We analyse the performance of the joint M2M and H2H system and investigate the trade-offs needed to keep satisfactory performance and reliability for M2M traffic in the presence of H2H traffic of known intensity. Our results confirm the validity of this approach for applications in crowd sensing, monitoring and others utilized in smart city development. Full article
(This article belongs to the Special Issue Wireless Sensor and Actuator Networks for Smart Cities)
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Open AccessArticle Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
J. Sens. Actuator Netw. 2018, 7(3), 26; https://doi.org/10.3390/jsan7030026
Received: 18 April 2018 / Revised: 13 June 2018 / Accepted: 13 June 2018 / Published: 4 July 2018
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Abstract
Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks
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Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results. Full article
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Open AccessArticle Fundamental Limitations in Energy Detection for Spectrum Sensing
J. Sens. Actuator Netw. 2018, 7(3), 25; https://doi.org/10.3390/jsan7030025
Received: 15 May 2018 / Revised: 27 June 2018 / Accepted: 27 June 2018 / Published: 28 June 2018
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Abstract
A key enabler for Cognitive Radio (CR) is spectrum sensing, which is physically implemented by sensor and actuator networks typically using the popular energy detection method. The threshold of the binary hypothesis for energy detection is generally determined by using the principles of
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A key enabler for Cognitive Radio (CR) is spectrum sensing, which is physically implemented by sensor and actuator networks typically using the popular energy detection method. The threshold of the binary hypothesis for energy detection is generally determined by using the principles of constant false alarm rate (CFAR) or constant detection rate (CDR). The CDR principle guarantees the CR primary users at a designated low level of interferences, which is nonetheless subject to low spectrum usability of secondary users in a given sensing latency. On the other hand, the CFAR principle ensures secondary users’ spectrum utilization at a designated high level, while may nonetheless lead to a high level of interference to the primary users. The paper introduces a novel framework of energy detection for CR spectrum sensing, aiming to initiate a graceful compromise between the two reported principles. The proposed framework takes advantage of the summation of the false alarm probability Pfa from CFAR and the missed detection probability (1Pd) from CDR, which is further compared with a predetermined confidence level. Optimization presentations for the proposed framework to determine some key parameters are developed and analyzed. We identify two fundamental limitations that appear in spectrum sensing, which further define the relationship among the sample data size for detection, detection time, and signal-to-noise ratio (SNR). We claim that the proposed framework of energy detection yields merits in practical policymaking for detection time and design sample rate on specific channels to achieve better efficiency and less interferences. Full article
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