Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g., cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

Fault-Prone Mobile Edge Cloud (FP-MEC) is a new type of distributed network composed of mobile edge computing and network function virtualization, where virtual network services can be provided in the form of service function chains (SFCs) that are a sequence of virtual network functions (VNFs) on-demand deployed on resource-limited edge servers. FP-MEC has a characteristic that the fault probability of each VNF is dynamic and fluctuates with time and workloads, making SFCs temporarily unreliable. To increase the reliabilities, redundant Backup VNFs (BVNFs) need to be deployed near the VNFs and activated when they experience faults. Different mobile users would request different SFCs with reliability and service time demands to process their data. However, workloads of VNFs are dynamic and unpredictable in FP-MEC due to random arrival of user requests. How to optimally deploy VNFs and corresponding BVNFs on a set of edge servers to form expected SFCs that have higher reliabilities than user demand values, meanwhile throughput of receiving requests is maximized while receiving cost is minimized in real-time, is a challenging problem. The receiving cost is composed of deployment cost of instantiating VNFs and BVNFs, and communication cost of routing data among users, VNFs and BVNFs. In this paper, the long-term provisioning problem is first formulated as an integer linear program and proved to be NP-hard. Then, it is discretized into a sequence of one-slot optimization problems to handle practical time-varying fault probability, where a set of SFC requests are given at each time slot, and receiving or rejecting decisions are executed immediately without any future information. Finally, an online approximation scheme with a constant approximation ratio is proposed to solve the one-slot problems in polynomial time. Theoretical analyses and experiments based on real network topology of CERNET in China demonstrate that the scheme is promising compared to existing works.

Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.

The rapid progress in edge computing (EC) and 5G wireless communication technology has opened up novel opportunities for intelligent applications driven by Deep Neural Networks (DNNs). In particular, machine vision tasks are widely used in mobile/edge computing scenarios. However, the realtime and dense data transmission involved in vision inference services impose significant communication burdens on wireless networks. Thus, this paper investigates the general vision services strategy with cognitive computing network and proposes a communication-efficient edge inference deployment architecture for vision analytic tasks. In this framework, users dynamically perceive the inference data in local, and then compress and offload them to the edge server to perform inference. Specifically, we present a collaborative optimization model of compression ratio and network bandwidth to generate the reliable compression offloading and resource allocation scheme. For this model, the offloading scheme carefully considers the constraints imposed by delay and resources and maximizes the success probability of the vision inference tasks. To improve the vision inference performance in the edge network, we further propose a flexible data compression algorithm for images or video frames, which can preserve the more important visual information under a fixed compression rate to reduce the inference accuracy loss from compression. This algorithm first perceives the importance of visual information at different pixel positions, and then compresses different visual regions to varying degrees according to their importance, enabling content-aware adaptive vision data coding. Experimental results show that our proposed offloading model and compression strategy outperform other algorithms, achieving significant communication improvements and performance gains.

With the rapid development of edge intelligence cloud (EIC), mobile users are not satisfied with a single artificial intelligence inference service, but require multiple inference services with chain dependencies to process data. Each AI service chain (AISC) is provided as a series of interconnected virtual network functions (VNFs) on-demand deployed on edge servers. However, AISCs experience unpredictable failures and potential attacks in EIC, which may violate different inference requirements of mobile users for reliability, security, and accuracy. How to optimally deploy VNFs and BVNFs on trusted edge servers, and select secure links to form satisfactory AISCs, meanwhile throughput of receiving requests is maximized while deployment cost of computing resources used to create VNFs and BVNFs with different model sizes is minimized in real-time, is a challenging problem. In this paper, the problem is first formulated as an integer linear programming and proved to be NP-hard. Then, we consider the problem under two online backup scenarios: one is an on-site scenario where AISC requests from the mobile devices arrive one by one, and link securities between VNFs and corresponding BVNFs are ignored because they are always on the same edge server; another is an off-site scenario where a set of AISC requests are given, and VNFs and BVNFs are deployed on different servers. Finally, two online algorithms with provable competitive ratios are proposed to solve the above two problems in polynomial time. Theoretical analyses and experiments based on real network topologies demonstrate that our algorithms are promising compared to baseline algorithms.