LoRaWAN Performance Evaluation and Resilience under Jamming Attacks

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization.Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective.This paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter.This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration.Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm.The proposed algorithm was applied using eight beacons.The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.

Smart metering utility network (SUN) is an emerging wireless technology for achieving intelligent control and information transfer in smart grid applications. However, the operation of SUNs on unlicensed bands is very sensitive to external interferences generated from other networks working on the same spectrum bands, such as wireless local area networks (WLANs). This paper aims to analyze packet error rate (PER) performance of a victim SUN receiver under the interferences of one or more WLAN transmitting nodes. To deal with a scenario leveraged by multiple random variables, such as offset between desired SUN data packet and WLAN packet, duration of WLAN data packet, and time interval between two consecutive WLAN packets, we propose a segmented packet collision model with the help of the probability theory. We first divide all the packets into small segments and then calculate the collision probability of each segment to acquire a more accurate bit error rate (BER). Then, based on different BERs in distinct segments of the desired SUN packet, average PER of the victim SUN receiver is obtained. Finally, the proposed analytical model is validated by simulations as an effort to evaluate PER performance of a SUN receiver under the WLAN interferences.

Long Range Wireless Access Network (LoRaWAN) emerged as one of the promising Low Power Wide Area Networks (LPWAN) for IoT applications. It allows end-devices to reach a gateway and then the core network with a star topology in a wide area. Long Range (LoRa) transceivers send data packets according to a configuration or a set of parameter’s values: Spreading Factor (SF), Payload size (PS), Bandwidth (BW) and Coding Rate (CR). These parameters must be fixed or adapted to application’s requirements. Adaptive Data Rate (ADR) control system of LoRaWAN has been proposed to adapt modulation parameters dynamically based on the recent received packets. However, ADR control system doesn’t adjust parameters considering the evolution of applications’ Quality of Service (QoS) requirements. In this paper, we propose to cluster a set of LoRa transmission settings based on the measured QoS metrics such as Bit Error Rate (BER), Time on Air (ToA) and Received Signal Strength Indication (RSSI). We consider the set of settings’ vectors as a cloud of points in a vector space while measured metrics are points’ coordinates. Our method aims to map a set of LoRa transmission settings that offers the same QoS to the same cluster. We generate a set of transmission settings randomly and apply the Fuzzy C-Means (FCM) clustering algorithm on the resulting QoS metrics, Results show that the FCM clustering algorithm attribute membership values that best fit application requirements. This result could be used by LoRaWAN network servers to map each LoRa transmission setting to the application running on end devices.

This paper focuses on the experimental validation of the Fuzzy Logic Based Routing Algorithm (FLBRA) proposed for wireless sensor networks which operates in indoor environments in the context of building management systems. The fuzzy logic determines the cost of each link for the routing algorithm and calculates the lower cost path for packet forwarding. The network parameters used to determine the cost of each link through the Fuzzy Logic are the Received Signal Strength Indicator (RSSI), the standard deviation of the RSSI and the packet error rate. The routing algorithm based solely on the RSSI was also tested and compared to the FLBRA one. The proposal performance was evaluated using different scenarios in which different numbers of sensor nodes were considered in the same indoor environment. Both FLBRA and RSSI-based solutions were monitored and analyzed using the same testbed. It can be seen from the results that the FLBRA has a better performance, selecting the best routes with lower cost, in comparison to the solution solely based on the RSSI.

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.

Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

The performance of wireless communication systems is fundamentally determined by the properties of the underlying wireless communication channel. Vehicular communication channels exhibit time-variant multi-path propagation with non-stationary channel statistics. Thus, channel emulation tools for the reproducible test of wireless communication systems are urgently needed to enable the development of ultra-reliable low-latency communication links. In this paper we validate the vehicle-in-the-loop (ViL) test of vehicle-to-everything (V2X) communication links by means of time-variant channel emulation. The validation is performed by comparing the received signal strength indicator (RSSI) and packet error rate (PER) of measurements on a proving ground with the RSSI and PER obtained from ViL tests. For the ViL tests, the wireless communication channel is emulated using a geometry-based stochastic channel model, which is updated in real-time, dependent on the position and velocity of the vehicles. We collect results of different scenarios on the proving ground and from ViL tests. The results show an qualitative match between ViL test and measurement on the proving ground. An exact quantitative match can be obtained with the calibration parameters from the measurements.

Error performance is a critical factor to support reliable communication links. In this paper, we measure packet error rate (PER) of vehicular communication systems when the adjacent channel interferences exist. The measurement is carried out in the rural area. We use IEEE 802.11p standard for vehicular communications. First, we briefly review the physical layer properties of IEEE 802.11p standard. Then, we represent the measurement results of received signal strength indicator (RSSI). Finally, the PER measurement results are introduce by considering the adjacent channel interferences. Based on the measurement results, we discuss the reliability of vehicular communication systems.KeywordsVehicular communicationsIEEE 802.11perror performancechannel interference

The paper evaluates the performance of a CDMA based wireless sensor networks (WSN) with layered architecture in terms of bit error rate (BER) and energy consumption for successful transmission of data packets. Correlation amongst signal and interferers in shadowed environment has been considered. Two kinds of interference namely multiple access interference (MAI) and node interference (NI) are considered. The bit error rate (BER) and packet error rate (PER) of the received signal are evaluated and energy required for successful delivery of information to the sink considering single hop communication is estimated. The effects of node density, correlation amongst interferers and power control error on BER, PER, average number of retransmission and energy consumption for successful reception of a packet at sink in a single hop are investigated. Analytical results are verified through simulations.

Machine-to-Machine technology supports several application scenarios, such as smart metering, automotive, healthcare and city monitoring. Smart metering applications have attracted the interest of companies and governments since these applications bring many benefits (e.g. costs reduction and increased reliability) for production, monitoring and distribution of utilities, such as gas, water and electricity. Multi-hop wireless communication is a cost-effective technology for smart metering applications because it extends the wireless range and enables fast deployment. Smart metering data communicated via wireless multi-hop approaches needs mechanisms that makes the communication less vulnerable to security threats and saves the device resources. Data encryption and data aggregation mechanisms emerge as potential solutions to fulfill these requirements. However, the simultaneous execution of data encryption and data aggregation mechanisms is not a trivial task. This is because the data encryption prevents the data aggregation mechanism to summarize the data along the path. Another challenge is to manage both mechanisms according to the concurrent Machine-to-Machine (M2M) applications interests. In this context, we present sMeter, which is a framework that deals with multiple applications interests, avoiding interest conflicts of concurrent users and supporting the management of data aggregation and data encryption. sMeter is implemented using low-cost hardware in an indoor environment. The communication is performed via a wireless multi-hop technology, and the performance of this communication is evaluated in terms of delay, data reception ratio and received signal strength indication.

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad hoc network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routes update, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results.

As a part of the EU-FP7 R&D programme, the OMEGA project (hOME Gigabit Access) aims at bridging the gap between wireless terminals and wired backbone network in homes, providing high bit rate connectivity to users. Beside radio frequencies, the wireless links will use Optical Wireless (OW) communications. To guarantee high performance and quality of service in real-time, our system needs techniques to approximate the Bit Error Probability (BEP) with a reasonable training sequence. Traditionally, the BEP is approximated by the Bit Error Rate (BER) measured by counting the number of errors within a given sequence of bits. For small BERs, required sequences are huge and may prevent real-time estimation. In this paper, methods to estimate BER using Probability Density Function (PDF) estimation are presented. Two a posteriori techniques based on Parzen estimator or constrained Gram-Charlier series expansion are adapted and applied to OW communications. Aided by simulations, comparison is done over experimental optical channels. We show that, for different scenarios, such as optical multipath distortion or a well designed Code Division Multiple Access (CDMA) system, this approach outperforms the counting method and yields to better results with a relatively small training sequence.

In this developing paradigm, heterogeneous network infrastructure is combined with ubiquitous devices for sensing, smart control systems and big data analytics to create “smart cities.” Enhancing the overall quality of life is one of their main goals and they do so through smart services they offer in a broad range of areas such as transportation, healthcare, entertainment (including virtual reality), the environment (including pollution) and energy. To be able to offer those services, the job of big data and their analysis is complicated, since it allows for the extraction of useful insights from the enormous amounts of data that the smart cities produce. The current state-of-the-art research efforts aimed at big data analytics in a smart city setting are investigated and presented in this chapter. In particular, a comprehensive literature review on the big data analytic frameworks for smart cities is provided. Following that, a short talk on IoT and big data is briefed. Owing to different data that the smart cities collect, which frequently have contradictory processing needs, appropriate investigative methods based on the data type are also recommended, depending on the data type. The many smart city applications were investigated and a perspective on the difficulties associated with smart city development was developed. In this chapter, the methods addressing the smart city issues and development are examined, and the possibility for the employment of integrated IoT and big data analytics is presented. Finally, as a case study, a briefing on a call service application for the fire department in a smart city is provided to round up the presentation.

Protect your data and I’ll rank its utility: A framework for utility analysis of anonymized mobility data for smart city applications