Secure containers leverage hardware virtualization to isolate container sandboxes, enabling dedicated guest kernels to mitigate shared kernel attacks prevalent in traditional systems. However, existing approaches struggle with a fundamental trade-off: VM-based solutions (e.g., Kata) prioritize performance but lack elasticity and on-demand usage for volatile and bursty workloads, while lightweight methods (e.g., gVisor) rely on the host kernel for dynamic resource management at the cost of significant performance degradation due to guest-host dependencies.

Industrial wireless networks (IWNs) are being increasingly deployed in the field to serve as the network fabrics for various industrial Internet-of-Things (IIoT) applications. Given that IWNs typically operate in noisy and harsh environments, frequently occurring network dynamics post huge challenges for IWN resource management especially when the network scales up. Existing centralized and distributed network management solutions either suffer from large communication overhead and time delay, or introduce schedule collisions which unnecessarily degrade the system performance. To address these problems, this work proposes a novel HierArchical Resource Partitioning framework (HARP), to provide dynamic resource management in IWNs. By hierarchically partitioning and allocating resources for the links in the network, HARP enables distributed collision-free resource allocation. HARP enables rapid adjustment of the partitions in the presence of network dynamics with modest communication overhead. The effectiveness of HARP is validated and evaluated through both simulation studies and testbed experiments on a 50-node multi-channel multi-hop 6TiSCH network.

With the rapid growth of industrial Internet of Things (IIoT) applications, real-time wireless networks (RTWNs) are playing an increasingly important role in providing realtime, reliable, and secure communication services for these applications. A key challenge in RTWN management is to ensure real-time Quality of Services (QoS), especially in the presence of unexpected external (i.e., application-side) and internal (i.e., network-side) disturbances. This paper presents a novel framework, DS-PaS, to determine the packet transmission schedule for multi-channel multi-hop RTWNs at the data link layer in a distributed and dynamic fashion. DS-PaS is able to (i) handle external disturbances, (ii) support spatial reuse, (iii) meet deadlines of all critical tasks, and (iv) minimize the number of dropped non-critical packets. To avoid transmission collisions when using inconsistent information in a distributed framework, DS-PaS incorporates several key advances in both the data-link layer protocol and algorithm design so that individual nodes can build on-line schedules with only local interference information. Extensive evaluation based on both testbed implementation and simulation validates the correctness of the DS-PaS design and demonstrates its effectiveness compared to the state of the art.

The fifth-generation (5G) cellular networks are increasingly considered for industrial applications, such as factory automation systems. In 5G networks, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC) are two essential services. eMBB services require high data rates with some lower bounds while URLLC traffic is subject to strict latency and reliability requirements. Existing approaches to scheduling coexisting eMBB and URLLC traffic all assume that URLLC traffic preempts eMBB traffic immediately upon arrival, which can adversely impact the achievable eMBB data rates. Furthermore, none of the prior work considers guaranteeing minimum data rate requirements imposed on certain eMBB traffic. This paper proposes a new model to capture the URLLC and eMBB requirements and introduces a novel framework, QoSG-RA, to perform network resource allocation for coexisting eMBB and URLLC traffic. QoSG-RA builds on a hybrid offline/online approach which performs offline resource allocation to ensure the Quality of Service (QoS) requirements of eMBB and URLLC traffic to be satisfied and online resource allocation to maximize fairness on the data rates among eMBB traffic based on runtime information. QoSG-RA is able to (i) meet latency and reliability requirements of URLLC traffic, and (ii) maximize the data rates for eMBB traffic in a fair way while fulfilling their minimum data rate requirements. Experimental results demonstrate the effectiveness of QoSG-RA compared to the state-of-the-art.

The past decade has witnessed the rapid development of real-time wireless technologies and their wide adoption in various industrial Internet-of-Things (IIoT) applications. Among those wireless technologies, 6TiSCH is a promising candidate as the de facto standard due to its nice feature of gluing a real-time link-layer standard (802.15.4e, for offering deterministic communication performance) together with an IP-enabled upper-layer stack (for seamlessly supporting Internet services). 6TiSCH's built-in random slot selection scheduling algorithm, however, often leads to large and unbounded transmission latency, thus can hardly meet the real-time requirements of IIoT applications. This paper proposes an adaptive partition based scheduling framework, APaS, for 6TiSCH networks. APaS introduces the concept of resource partitioning into 6TiSCH network management. Instead of allocating network resources to individual devices, APaS partitions and assigns network resources to different groups of devices based on their layers in the network so as to guarantee that the transmission latency of any end-toend flow is within one slotframe length. APaS also employs a novel online partition adjustment method to further improve its adaptability to dynamic network topology changes. The effectiveness of APaS is validated through both simulation and testbed experiments on a 122-node multi-hop 6TiSCH network.

Currently, a number of crowdsourcing‐based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real‐time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software‐defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.

Information markets benefit the communities they serve by facilitating electronic distributed exchange and enhancing knowledge sharing, innovation, and productivity. This research explores innovative market mechanisms to build incentives while encouraging pro-social behavior. A key advantage of this study is a direct appeal to theories of information economics and macro policies to market design. We built and deployed a web-based software platform called Barter at several universities. Preliminary analysis of user data helps test information market effectiveness and illustrate effects of various market interventions. We present our design framework, demonstrate why such an architecture provides sustainable incentives, and list key findings learned in the process of system deployment.

Physical layer techniques have come a long way and can achieve very close to Shannon capacity for point-to-pint links. It is apparent that, to further improve network capacity significantly, we have to resort to concurrent transmissions. Many concurrent transmission techniques (e.g., zero forcing, interference alignment and distributed MIMO) are proposed in which multiple senders jointly encode signals to multiple receivers so that interference is aligned and each receiver is able to decode its desired information. In this paper, we investigate the constraints and challenges of using interference alignment. Our main contribution is conducting the first systematic investigation on the key issue of identifying opportunities for interference alignment. We identify diverse, novel scenarios for using interference alignment. We show that identifying opportunities for interference alignment in the general case is computational challenging. However, we also present a promising, distributed algorithm for identifying a wide range of opportunities for interference alignment using a unifying framework based on the degree of freedom. Our second contribution is evaluating key practical implementation issues.

In this paper, we present the Connected Consumption Network (CCN) that allows a community of consumers to collaboratively sense the market from a mobile device, enabling more informed financial decisions in geo-local context. The mobile application allows one to log one's wish list and itemized list of transactions to form a social network around the list of interests. Individuals can share this data to inform and guide others in a timely, personal and contextual manner when they are shopping for a product or seeking a service. It can also help people connect opportunistically in a local area to make group purchases, to pick up an item for a friend, and to perform reverse auctions. We present the design, architecture and concept prototype. We simulate a social network with three months of existing credit/debit card transaction data in various geographical areas to analyze the mutual information and recommendations that can be shared among networked consumers.

Users increasingly depend on WLAN for business and entertainment. However, they occasionally experience dead spots and high loss rates. We show that these problems can be addressed by exposing information readily available at the physical layer. We make the physical layer convey its confidence that a particular bit is 0 or 1 to the higher layers. Access points that hear the same transmission communicate their confidence values over the wired Ethernet and combine their information to correct faulty bits in a corrupted packet. A single receiver may also combine the confidence estimates from multiple faulty retransmissions to obtain a correct packet. We implement our design and evaluate it using GNU software radios. The results show that our approach reduces loss rate by up to 10x in comparison with the current approach, and significantly outperforms prior packet combining proposals.