With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

Bluetooth Low Energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBS) to user’s smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection, 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an N-dimensional fingerprint vector, which consists of N RSS values from N deployed beacons. The contribution of our proposed method is two-fold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value, 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with real-world datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

Multiple-input multiple-output (MIMO) visible light communication (VLC) systems can support high data rates for indoor communications. In this paper, we focus on maximizing the energy efficiency (EE) of MIMO VLC systems, where the non-negativeness of the transmit signal, the optical and electrical power constraints, and the data rate and bit error rate (BER) requirements are taken into account. The formulated problem is a difficult nonlinear fractional program. We exploit the Dinkelbach s algorithm and Lagrangian analysis to obtain a semi-closed form solution. The performance advantage of the proposed design is verified by simulation results.

In this letter, we propose a novel detector, called the sparse-aware minimum mean square error (SA-MMSE) detector, to improve detection performance for multiple input multiple output (MIMO) systems. We first convert a conventional MIMO system into a sparse system via the symbol error vector obtained from a linear detector. Then, we develop a new sparse refining method, which has a similar structure as the linear MMSE detector, to improve the detection performance. The proposed SA-MMSE detector can be performed iteratively and has low complexity similar to that of the parallel interference cancellation (PIC). Simulation results show that the SA-MMSE detector outperforms the compressive-sensing-based detector and PIC with a similar complexity, especially when the numbers of users and antennas become large.

This paper presents a novel bluetooth low energy (BLE) protocol enabling a BLE node to perform RF fingerprint acquisition by measuring the received signal strength (RSS) from its neighboring nodes and simultaneously broadcast the acquired fingerprint via its advertising packet. However, the fingerprint acquisition and broadcast process in a dense BLE network is very challenging owing to: 1) the likelihood of packet collision; and 2) the length-constrained packet. To this end, we exploit a compressive sensing (CS) framework allowing each node to acquire no more than $M$ measurements from a very dense network, in which the number of nodes $N$ is far greater than $M$ . By aggregating the $M$ -dimensional compressed fingerprint vector from $s nodes, the receiver is able to reconstruct the fingerprints broadcast by all $N$ nodes. This is the very first work that implements CS on a low power device for fingerprint acquisition. Our proposed compressive RF fingerprinting (CRF) has been demonstrated with real and dense BLE networks, consisting of 300 nodes randomly distributed around a confined area. We also conducted extensive simulations to verify the performance of three different reconstruction algorithms. The low reconstruction error at the receiving end indicates the feasibility of our proposed CRF in achieving efficient fingerprint acquisition for dense BLE networks.

With the development of sensor-clouds, the traditional WSN is expanded and the computing capacity is greatly improved. However, there are still challenges to be solved in sensor-clouds, such as how to disseminate codes to all nodes in a fast and energy-saving way. In this paper, an early wake-up ahead (EWA) code dissemination scheme is proposed to disseminate codes more efficiently. The main innovations of EWA code dissemination scheme are as follows: (a) An early wake-up message (EWM) routing protocol is proposed to propagate the early wake-up message ahead to awake the nodes in the routing path before data packets arrive. The forecast message can be forwarded for multi-hops during a time slot, while the packet can only be forwarded for one hop. Therefore, the nodes in the routing path can be awakened before the sending node starts to transmit the packets, the sleep latency is thus reduced as the sending node has not to wait for the receiver to be awakened for transmission. (b) The proposed EWA scheme aims at accelerating the code dissemination by increasing the duty cycle of the nodes which are far from the sink based on the EWM routing protocol. Meanwhile, the EWA scheme can improve the energy utilization rate without affecting the network lifetime. Theoretical analysis has verified the proposed protocol and scheme do accelerate code dissemination and improve energy utilization ratio. The experimental results show that the proposed EWA scheme reduces the network delay by 16.53%-37.13% compared to the conventional schemes.