Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency, and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.

A Survey of Device-to-Device Communications: Research Issues and Challenges

In futuristic wireless communications, a massive number of devices need to access networks with diverse quality of service (QoS) requirements. It is estimated that the number of connected devices will exceed 20 billions in 2020, and machine-to-machine (M2M) devices will account for nearly half of total connected devices. However, existing cellular systems and wireless standards, designed primarily for human-to-human (H2H) communications focusing on reducing access latency, increasing data rate, and system throughput, are not well suited for M2M communications that require massive connections, diverse QoS requirements, and low energy consumption. Radio resource management (RRM) in conventional H2H communications aims at improving spectrum efficiency and energy efficiency. Similarly, RRM also plays a vital role in M2M communications. In this paper, we make a comprehensive survey on state-of-the-art research activities on RRM in M2M communications. First, we discuss the issues on RRM for machine-type communications in LTE/LTE-A cellular networks including access control, radio resource allocation, power management, and the latest 3GPP standards supporting M2M communications. Acknowledging the fact that a single technology can not support all M2M applications, we discuss RRM issues for unlicensed band radio access technologies in M2M capillary networks, including IEEE 802.11ah, Bluetooth low energy, ZigBee, and smart metering networks. We also survey M2M RRM methods in heterogeneous networks consisting of cellular networks, capillary networks, and ultra dense networks. Finally, we review recent standard activities and discuss the open issues and research challenges.

Radio Resource Management in Machine-to-Machine Communications—A Survey

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests

Wireless Sensor Networks (WSNs) are most growing research area because of its low cost, infrastructure less, increase capabilities of nodes, real time and accurate. Localization is a major issue in the wireless sensor networks because it has a number of sensor nodes which are deployed at positions and they may not be fixed at their own position. In localization different techniques are used for distance and position estimation. In this paper we will focus on RSSI based localization in WSN which will show how to reduce location errors and improve accuracy by using various models and techniques. After that we will focus on how to make an algorithm scalable and improve the energy efficiency by providing authentication and key management.

RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey

To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this paper, we consider a two-phase cellular-based mMTC network, where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most of the cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

/pdf/massive-machine-type-communication-with-data-aggregation-and-2qogp4sbtb.pdf

Massive Machine Type Communication With Data Aggregation and Resource Scheduling

Traditional cellular networks have been considered the most promising candidates to support machine to machine (M2M) communication mainly due to their ubiquitous coverage. Optimally designed to support human to human (H2H) communication, an innovative access to radio resources is required to accommodate M2M unique features such as the massive number of machine type devices (MTDs) as well as the limited data transmission session. In this paper, we consider a simultaneous access to the spectrum in an M2M/H2H coexistence scenario. Taking the advantage of the new device to device (D2D) communication paradigm, enabled in long term evolution- advanced (LTE-A), we propose to combine M2M and D2D owing to the MTD low transmit power and thus enabling efficiently resource sharing. First, we formulate the resource sharing problem as a maximization of the sum-rate, problem for which the optimal solution has been proved to be non deterministic polynomial time hard (NP-Hard). We next model the problem as a novel interference-aware bipartite graph to overcome the computational complexity of the optimal solution. We propose two alternative algorithms, one centralized and one semi-distributed to perform the M2M resource allocation. The computational complexity of both introduced algorithms is of polynomial complexity. Simulation results show that the semi-distributed M2M resource allocation algorithm achieves quite good performance in terms of network aggregate sum-rate with markedly lower communication overhead compared to the centralized one.

https://hal-upec-upem.archives-ouvertes.fr/hal-01171611/document

Radio Resource Sharing for MTC in LTE-A: An Interference-Aware Bipartite Graph Approach

RSSI-based location estimation method in Wireless Sensor Networks (WSNs) present some advantages in terms of complexity and energy consumption. However, the propagation of the radiofrequency signals in indoor environments is subject to fading induced by shadowing effect and multipath propagation. Accurate wireless location estimation can be achieved by employing multiple antennas. In this paper, we make a comparison among three system models in order to show the impact of using multiple antennas on position accuracy at either the transmitter, the receiver side or at the both sides. We use the multilateration as well as the trilateration algorithms to calculate the position error. The obtained results illustrate that the localization performance is improved when using multiple antennas. Specifically, using multiple antennas at the both sides present better performance than using multiple antennas at either the transmitter or the receiver.

Comparative analysis of RSSI-based indoor localization when using multiple antennas in Wireless Sensor Networks

The introduction of machine-to-machine (M2M) communications to long term evolution and advanced (LTE-A) cellular networks can significantly degrade the performance of existing human-to-human (H2H) communications. In this paper, we consider a shared channel resource allocation in an H2H/M2M coexistence scenario. We first formulate the resource sharing problem between M2M and H2H communications as a bipartite graph (BG). In addition, we propose a power control scheme for the concurrently transmitting M2M nodes to mitigate the H2H performance degradation following a probability that is set based on a proportional integrative derivative (PID) controller reflecting the interference level. The impact of M2M radio resource allocation on the performance of conventional scheduling algorithms optimally designed for H2H communications in terms of data rate and fairness is evaluated. Simulation results are encouraging and our proposed scheme succeeds in reducing the impact of M2M communications on H2H services in terms of data rate and fairness.

A flexible M2M radio resource sharing scheme in LTE networks within an H2H/M2M coexistence scenario

Many localisation algorithms in wireless sensor networks WSNs are based on received signal strength indication RSSI. Although these methods present some advantages in terms of complexity and energy consumption, RSSI values especially in indoor environments, are very unstable due to fading. In this paper, we propose a comparative study of RSSI-based localisation algorithms using spatial diversity in WSNs. We consider different kinds of single/multiple antenna systems: single input single output SISO system, single input multiple output SIMO system, multiple input single output MISO system and multiple input multiple output MIMO system. We focus on the well known trilateration and multilateration localisation algorithms. Three diversity combining techniques at the receiver are used: maximal ratio combining MRC, equal gain combining EGC and selection combining SC. The obtained results show that using multiple antennas at both the transmitter and receiver sides present better performance than using multiple antennas at only one side. We also conclude that MRC diversity combining technique outperforms EGC that as well outperforms SC.

/pdf/rssi-based-localisation-algorithms-using-spatial-diversity-p79nqorqzy.pdf

RSSI-based localisation algorithms using spatial diversity in wireless sensor networks

Device-to-device (D2D) communications, one of the major component of the evolving 5G networks, is showing promising advantages on supporting machine-to-machine (M2M) communications. In this paper, we consider the design of efficient transmission strategy selection algorithm for M2M communications underlaying cellular networks. First, a group of machine type-devices (MTDs) is matched with a particular user equipment (UE). MTDs belonging to the same group can access the same spectrum within its matched UE while the latter quality of service (QoS) is maintained. Next, we propose an efficient evolutionary game based transmission strategy selection algorithm for M2M communications using D2D mode. Specifically, MTDs switch opportunistically from a non-cooperative strategy to a cooperative strategy. Initially, we consider a non-cooperative scenario due to the selfish behavior of devices. In case the latter QoS is not satisfied, MTDs switch to a cooperative game. In a cooperative game, we propose two alternative power control schemes: a fixed mixed-strategy power control scheme where each MTD willing to play cooperatively selects the power strategy from a discrete level of powers and an adaptive mixed-strategy power control scheme. The latter technique enables to set efficiently the discrete power levels using a fuzzy logic and a proportional-integral-derivative (PID) controllers aiming to assure the desired QoS of UEs while maximizing the efficiency of M2M communications. Simulation results show that the evolutionary game based transmission strategy selection algorithm avoids significant degradation of traditional human-to-human (H2H) services in terms of throughput and fairness compared to a single non-cooperative game strategy. Besides, the adaptive mixed-strategy power control scheme outperforms the fixed mixed-strategy power control scheme by saving the battery life of MTDs while guaranteeing the latter QoS.

Safa Hamdoun

Papers

Radio Resource Sharing for MTC in LTE-A: An Interference-Aware Bipartite Graph Approach

Comparative analysis of RSSI-based indoor localization when using multiple antennas in Wireless Sensor Networks

A flexible M2M radio resource sharing scheme in LTE networks within an H2H/M2M coexistence scenario

RSSI-based localisation algorithms using spatial diversity in wireless sensor networks

Efficient transmission strategy selection algorithm for M2M communications: An evolutionary game approach