Driven by emerging wireless applications, numerous smart objects will be deployed, forming things networks interconnected by the Internet of Things (IoT). Many of these networks follow the low-power multihop wireless network (LPMWN) paradigm due to the good scalability of the distributed schemes adopted by LPMWNs. Although distributed schemes may operate satisfactorily most of the time, they are often complex and costly to manage. To simplify network management, the LoRa (long range) wireless technology has been introduced into LPMWNs for centralized network control. Unfortunately, due to LoRa’s inherent limitation that a downlink packet must be in response to a precedent uplink packet, the existing protocols typically require every node to periodically send an uplink packet to the gateway for downlink network command dissemination. This causes intensive uplink collisions and quickly depletes LoRa bandwidth, especially when the network is large. In light of these pitfalls, we propose a (cross-interface downlink relay (CDR)) scheme, which leverages the ZigBee communications that are already available in LPMWNs to relay LoRa downlink data traffic for bandwidth-efficient and delay-guaranteed data dissemination. CDR determines the optimal network topology for bandwidth-minimized data relay while adjusting system parameters to ensure each node’s delay requirement under network dynamics. A prototype system is implemented by integrating LoRa and ZigBee into an IoT platform. Extensive outdoor experiments show that the bandwidth consumption of CDR is 39.3% lower than a state-of-the-art LoRa data dissemination protocol for ZigBee-based LPMWNs under a moderate data traffic and delay requirement. Besides, CDR improves energy efficiency and network lifetime significantly.

The wireless sensor network (WSN) is a multi-hop wireless network that comprises multiple sensor nodes arranged in a self-organized manner. It is usually deployed in unattended areas where sensor nodes can easily be infiltrated by attackers who can affect the detection results by injecting false data. This paper proposes a malicious-node identification method based on correlation theory that prevents fault data injection attacks. First, anomalies among similar types of sensor data are detected based on time correlation. Second, malicious nodes are identified based on spatial correlation. Third, the identified malicious nodes are verified based on event correlation. The experimental results and their comparison with those of existing methods show that the proposed scheme has better recall with lower false-positive and false-negative rates than those of the traditional fuzzy reputation model and weighted-trust-based methods.

Featured with high bandwidth, high reliability, and native IP compatibility, WiFi has been recommended for a wide range of Internet-of-Things (IoT) applications. However, WiFi is inherently energy-hungry and it may impose high energy consumption on not only IoT devices but also gateways. To reduce gateway's WiFi energy consumption, many energy-efficient solutions for WiFi tethering services can be applied. However, these solutions mainly target the energy optimization of downlink data traffic in WLANs, and they are not suitable for the uplink data traffic of delivering massive IoT data from device to gateway (D2G), which is more common in IoT. When a gateway is powered by batteries, the high energy consumption caused by D2G communications may deplete the gateway quickly and renders the whole system dysfunctional as a result. Toward achieving energy-efficient D2G communications, we propose an innovative Green IoT Gateway (GIG) scheme, which aims at minimizing gateway energy consumption while ensuring the specific delay requirements of devices via cross-interface collaboration. Through utilizing the coexisting low-power ZigBee radios, GIG dynamically schedules the wakeup behaviors of high-power WiFi radios for energy-efficient and delay-bounded D2G communications. GIG has been implemented and evaluated in a prototype system, and the experiment results show that, under the moderate uplink data traffic and delay requirements, the energy consumption of GIG is 38.5% and 12.7% lower than those of a state-of-the-art WiFi tethering scheme and a simple version of the GIG scheme, respectively. Moreover, a great reduction of energy consumption at the device side can also be observed.

Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.

The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11's standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.

WiFi transmission can consume much energy on energy-constrained mobile devices. To improve energy efficiency, the Power Saving Management (PSM) has been standardized and applied. The standard PSM, however, may not deliver satisfactory energy efficiency in many cases as the wakeup strategy adopted by it cannot dynamically adapt to traffic pattern changes. Motivated by the fact that it has been more and more popular for a mobile device to have both WiFi and other low-power wireless interfaces such as Bluetooth and ZigBee, we propose a ZigBee-assisted Power Saving Management (ZPSM) scheme, leveraging the ZigBee interface to wake up WiFi interface on demand to improve energy efficiency without violating delay requirements. The simulation and prototype-based experiment results have shown that ZPSM can save energy significantly without violating delay requirements in various scenarios.

In emergency situations, infrastructure-based wireless access networks (e.g., WLANs, cellular networks) may collapse due to power outage or infrastructure damages. To enable responsive communication for rescue operations, wireless ad hoc networks may be set up. However, due to limited battery capacity, the networks may not sustain long enough, especially when stringent communication delay is demanded. Aimed at energy efficiency, the Power Saving Management (PSM) for IEEE 802.11 DCF has been standardized and can be applied in multi-hop ad hoc networks. With PSM, however, it is difficult to achieve high energy efficiency and low delay simultaneously when data traffic is unpredictable. Motivated by the trend that low-power ZigBee interface will be more and more commonly embedded in mobile devices together with WiFi interface in the near future, we propose a ZigBee-assisted PSM (called ZPSM) for DCF, which leverages ZigBee interfaces to wake up WiFi interfaces on demand to reduce WiFi interfaces' energy consumption and thus prolong network lifetime, while satisfying delay requirements. The results of extensive simulation and prototype-based experiments have verified the performance advantages of ZPSM.

WiFi transmission can consume much energy on energy-constrained mobile devices. To improve energy efficiency, the Power Saving Management (PSM) has been standardized and applied. The standard PSM, however, may not deliver satisfactory energy efficiency in many cases as the wakeup strategy adopted by it cannot adapt dynamically to traffic pattern changes. Motivated by the fact that it has been more and more popular for a mobile device to have both WiFi and other low-power wireless interfaces such as Bluetooth and ZigBee, we propose a ZigBee-assisted Power Saving Management (ZPSM) scheme, leveraging the ZigBee interface to wake up WiFi interface on demand to improve energy efficiency without violating delay requirements. The simulation results have shown that ZPSM can save energy significantly without violating delay requirements in various scenarios.

Improving driving safety is one major objective of forming vehicular ad hoc networks (VANETs). Existing VANETs usually assume drivers detect and report safety-related road conditions. However, drivers may not be willing to perform these duties; even they are, these duties may distract them from driving and thus make driving unsafe. To address the problem, this paper proposes an automatic detection system. By taking advantage of the communication capability of roadside sensors, the proposed system can automatically detect and locate problematic road conditions without any human intervention under varying traffic densities. Extensive simulations have been conducted to verify the efficiency of the proposed system.

One major goal of the vehicular ad hoc network (VANET) is to improve driving safety. However, the VANET may not guarantee timely detection of dangerous road conditions or maintain communication connectivity when the network density is low (e.g., in rural highways), which may pose as a big threat to driving safety. Towards addressing the problem, we propose to integrate the VANET with the inexpensive wireless sensor network (WSN). That is, sensor nodes are deployed along the roadside to sense road conditions, and to buffer and deliver information about dangerous conditions to vehicles regardless of the density or connectivity of the VANET. Along with the concept of VANET-WSN integration, new challenges arise and should be addressed. In this paper, we investigate these challenges and propose schemes for effective and efficient vehicle-sensor and sensor-sensor interactions. Prototype of the designed system has been implemented and tested in the field. Extensive simulations have also been conducted to evaluate the designed schemes. The results demonstrate various design tradeoffs, and indicate that satisfactory safety and energy efficiency can be achieved simultaneously when system parameters are appropriately chosen.

This study investigates the effects of game difficulty on player’s immersion. Key factors in this study are difficulty of direction changes, including three directions (up and down, down and up, and continuously increasing) and difficulty of rate changes, with three rates (slow, medium, and fast). An experiment was conducted with 48 participants, each playing the same experimental games with different difficulty of direction or rate changes. The results indicate that the players have better immersion when the difficulty changes up and down than when it changes down and up or when the difficulty is continuously increased. And the participants have better immersion when the difficulty changes at a medium rate than when it changes slowly or fast.

In order to measure player immersion in the computer game narrative, this study explores and analyzes factors of the computer game narrative that influence players who are immersed in the game story world. Originally a questionnaire consisting of six dimensions—Curiosity, Concentration, Challenge, Control, Comprehension, and Empathy—was proposed. To evaluate the questionnaire, two surveys were conducted on the Internet, and data were collected from 734 respondents. After exploratory factor analysis and confirmatory factor analysis, the dimensions were modified to Curiosity, Concentration, Challenge and Skills, Control, Comprehension, Empathy, and Familiarity.