Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared have been used to localize and track people living in care homes and office buildings. However, the presence of multiple residents introduces further challenges, such as the ambiguity in sensor measurements and target identity, to localization and tracking. In this article, we survey the latest development of device-free indoor localization and tracking in the multi-resident environment. We first present the fundamentals of device-free localization and tracking. Then, we discuss and compare the technologies used in device-free indoor localization and tracking. After discussing the steps involved in multi-resident localization and tracking including target detection, target counting, target identification, localization, and tracking, the techniques related to each step are classified and discussed in detail along with the performance metrics. Finally, we identify the research gap and point out future research directions. To the best of our knowledge, this survey is the most comprehensive work that covers a wide spectrum of the research area of device-free indoor localization and tracking.

A Survey on Device-free Indoor Localization and Tracking in the Multi-resident Environment

with the use of small lightweight sensors with distributed intelligence, IoT is considered to be the next step in the Internet evolution. As per European Commission, in the coming years, the integration of Internet with wireless communications and embedded wireless sensor networks will provide a paradigm shift in transforming our everyday devices into intelligent and context-aware, Devices with sensing capabilities are in the form of smart objects, where wireless sensor in IoT is expected to play a signifiant role. Scalability is the ability of the product to perform well besides the size of the device or volume of the data. The next wave of Internet is how things get done without intervention of humans. There are more exciting applications like agriculture When optimizing WSNs, it is necessary to consider both the problem and the solution. The type of problem and solution will depend on factors such as the optimization goal or network model. In this proposed work, we propose a clustering approach based on the Fuzzy method and applied to agriculture, we focus on network and data link-level optimizations, and energy consumption optimization white maximizing node density and coverage area for scalable scenarios The result elaborate by our simulations prove that the proposed algorithm outperforms in terms of dead last node and half of the nodes dead for scalable scenarios. Thus, it can be used in IoT systems.

Dynamic Cluster Head Selection Method for Wireless Sensor Network for Agricultural Application of Internet of Things based Fuzzy C-means Clustering Algorithm

Wireless sensor networks (WSNs) are applicable in numerous fields ranging from very common to those demanding critical security concerns. It is recommended to provide external users a direct access to the data from sensors as per their need for real-time applications. To avoid unauthorized access, user authentication is essential for accessing the services between the nodes. Recently, Tai et al. designed an authentication scheme providing user anonymity, though it is found that Tai et al. are susceptible to attacks like offline password guessing, privileged insider, and known session-specific temporary information. Furthermore, it does not provide user anonymity and the secret key of gateway node is insecure. To address these security concerns, “Rabin cryptosystem”–based authentication scheme is proposed. It considers three factors for authentication, namely key generation, encryption, and decryption. During key generation, it takes two prime numbers as private and public key is computed as product of private keys. For encryption steps, only public key is used. The Blum-Blum-Shub pseudo-random bit generator algorithm and private key are used in the decryption process. The proposed scheme provides better security features because of its strong integer factorization which also increases the efficiency of the encryption and decryption process. It is apparent from security analysis of the RCBE-AS that it is free from attacks. This scheme is also simulated formally with AVISPA that shows that it is safe against cryptographic attacks. The proposed scheme also has less communication cost as compared to other related schemes. This scheme uses a random oracle model and automated tool, AVISPA, for formal security analysis to prove its efficacy. Furthermore, it provides mutual authentication using BAN logic.

RCBE-AS: Rabin cryptosystem–based efficient authentication scheme for wireless sensor networks

Device-free localization (DFL) plays an increasingly dominant role in some security and military applications, aiming at locating targets without carrying any extra devices. Most research utilize received signal strength, causing poor accuracy due to insufficient information. In this letter, we propose DF-CSPG, a new DFL algorithm using compressive sensing (CS) theory and potential game (PG) approach to exploit channel station information (CSI). It is the first time that the PG approach is applied in DFL. At first, the CS-based localization model is built by exploiting CSI and then investigated from a game-theoretic perspective in which the subcarriers are assumed as players. Then, we prove that the proposed DFL game is an exact PG. Based on these, the best response theory is utilized to obtain the equilibriums. Finally, simulation results show that DF-CSPG can achieve more accurate and robust performance.

DF-CSPG: A Potential Game Approach for Device-Free Localization Exploiting Joint Sparsity

In recent years, most of Lora's research in the field of wireless technology has focused on the experimental exploration of its stability and communication range, without much attention to the impact of the actual transmission rate. However, as the length of Lora transmission load increases, the growth rate of low-speed transmission time is faster than that of high-speed transmission time. Therefore, it means that for the transmission of large capacity packets, the lower the transmission rate, the more time it takes; the longer the time, the more power it consumes. In this paper, the SNR, RSSI and packet loss rate of Lora over 3 Kbps in urban area and mountainous area were tested and explored respectively, and the RSSI curve with distance in different environment was obtained. The test results show that the actual communication distance of Lora with high transmission rate of 21875 bps in line of sight environment can reach 3 Km, packet loss rate is less than 1%. In addition, the influence of Lora on communication quality under different occlusion degrees in a certain range was tested. In the range of 500 m to 3 km, when the occlusion ratio was 0-5%, the highest speed reference value was 21875 bps, 5-10% reference value was 6250 bps, and more than 10% reference value was 3125 bps. In this paper, Lora SX1278 transceiver was used, in the same transmission of 100 KB data, the actual transmission rate is further improved by using the maximum duty cycle and improving the request response mechanism. Compared with a Lora system for image transmission, the transmission time is reduced by 81.3% and the transmission speed is increased by 4.3 times, which greatly improves the transmission efficiency of large capacity data such as image.

https://robots.iopscience.iop.org/article/10.1088/1742-6596/1544/1/012177/pdf

Performance Research of LoRa at High Transmission Rate

Wireless sensor networks (WSNs) have been used for many long-term monitoring applications with the strip topology that is ubiquitous in the real-world deployment, such as pipeline monitoring, water quality monitoring, vehicle monitoring, and Great Wall monitoring. The efficiency of routing strategy has been playing a key role in serving such monitoring applications. In this article, we first present a robust geographic opportunistic routing (GOR) approach—LIght Propagation Selection (LIPS)—that can provide a short path with low energy consumption, communication overhead, and packet loss. To overcome the complication caused by the multi-turning point structure, we propose the virtual Plane mirror (VPM) algorithm, inspired by the light propagation, which is to map the strip topology into the straight one logically. We then select partial neighbors as the candidates to avoid blindly involving all next-hop neighbors and ensure the data transmission along the correct direction. Two implementation problems of VPM—transmission spread angle and the communication range—are thoroughly analyzed based on the percolation theory. Based on the preceding candidate selection algorithms, we propose a GOR algorithm in the strip topology network. By theoretical analysis and extensive simulation, we illustrate the validity and higher transmission performance of LIPS in strip WSNs. In addition, we have proved that the length of the path in LIPS is two times the length of the shortest path via geometrical analysis. Simulation results show that the transmission success rate of our approach is 26.37% higher than the state-of-the-art approach, and the communication overhead and energy consumption rate are 33.11% and 40.23% lower, respectively.

Low-Cost and Robust Geographic Opportunistic Routing in a Strip Topology Wireless Network

Wireless localization technology is a vital component in many long-term monitoring applications, such as activity monitoring and real-time tracking. Most existing localization methods however require the target to carry communicationcapable devices to send or receive messages, which may not hold for wildlife monitoring or intrusion detection. Prior proposals are based on device-free localization techniques, such as Channel State Information (CSI). However, they cost huge human effort in fingerprint collection when locate the target in different scenarios with different area size. This paper proposes a robust and accurate at low-cost devicefree localization system named LiReT. To reduce the time cost and human effort in fingerprint collection when the monitoring environment changed, we represent a LiReT algorithm based on a multivariable linear regression model to transfer the CSI measurements (fingerprint) at distance L to L'. Thus, LiReT can locate the target accurately at low-cost. Result from experiments demonstrate that our system can improve the localization accuracy by up to 51.68%, which is competitive with existing solutions.

Xinyan Liu

Papers

Low-Cost and Robust Geographic Opportunistic Routing in a Strip Topology Wireless Network

LiReT: An Fine-Grained Self-Adaption Device-Free Localization with Little Human Effort