For various wireless sensing and IoT applications, LoRa has emerged as an energy-efficient and long range solution for wireless data transfers. However, real-world deployments face multiple challenges, including packet collisions and variable link qualities, which generally lead to packet corruptions and data loss. Even though LoRa relies on forward error correction to restore corrupted packets, its usage comes with a significant energy overhead and only provides limited capabilities. In our paper, we present CEC-LoRa as an alternative to LoRa’s forward error correction feature. Its key innovation is that CEC-LoRa allows to restore corrupted packets without any error correction codes. Our approach exploits two symbiotic properties in LoRa’s encoding and chirp-based modulation scheme. On the one hand, misinterpreted chirps are often confused with neighboring symbols. On the other hand, neighboring symbol values only differ by one, keeping high similarity between misinterpreted and correct symbols. We leverage these properties by pre-computing a set of all plausible LoRa packets based on the expected payload permutations and compare them with the received packet. This allows us to identify the closest match and thus the most probable candidate packet. While the pre-computations incur a quite substantial energy overhead, CEC-LoRa shifts this from the energy-constrained end devices to the receiver side. The benefits greatly outweigh the energy demand, though: CEC-LoRa reduces the number of corrupted packets by 86.5 % on average without any additional overhead on the end device and while still being fully compliant to the LoRa specification. By including symbol information, CEC-LoRa can reduce the number of corrupt packets even further, by 99.5 % on average. This translates into SNR gains in the range of 0.79 dB to 0.93 dB.

The rising popularity of the Internet of Things and wireless sensing applications calls for energy-efficient and scalable communication protocols. LoRaWAN is one of today’s most prominent choices for this purpose, as it features long communication ranges and high energy-efficiency. However, a major drawback is its limitation to being operated in a star topology. Thus, data originating from nodes outside of the single-hop neighborhood of a gateway cannot be received. We overcome this limitation by presenting Backpack-LoRa, an extension to LoRaWAN which enables multi-hop uplink communication while being compatible with existing deployments. Instead of simply re-transmitting overheard frames entirely, however, Backpack-LoRa nodes extract and buffer only the relevant frame content for later decryption and append it to their own frames, which are transmitted during the next regular transmission slots. To address the limitation of most commodity transceivers – only being able to listen on one out of multiple available channels – we propose a pseudo-random channel allocation algorithm based on time and device address for Backpack-LoRa. We implement our approach in a real-world outdoor deployment consisting of four nodes and gateways. Subsequently, we analyze the improvements in network coverage, packet reception rate, and energy overhead. Our results show that Backpack-LoRa enables nodes to reach gateways which were unreachable for standard LoRaWAN and increase the packet reception rate by up to 54.9 %. As compared to a simple re-transmission approach, Backpack-LoRa can reduce the energy overhead by 28.8 %.

In the world of IoT and wireless sensor networks, LoRaWAN has established itself as a quasi-standard for energy-efficient and long-range wireless communication in recent years. Thanks to its chirp-based modulation, receivers can demodulate packets with negative signal-to-noise ratios (SNRs), even down to −20 dB. As a result of changing ambient conditions, however, transmitted signals can experience increased attenuation, due to which they may be received with SNRs below the demodulation threshold, leading to packet loss. In this paper, we present an alternative demodulation approach that makes it possible to receive packets significantly below LoRa’s standard SNR threshold, which we call PreCo. In contrast to standard LoRa, which demodulates a packet symbol-by-symbol, our approach compares the received signal to a set of pre-computed LoRa packets in order to identify the closest match. We efficiently compute the set of packets against which the comparisons are performed based on the expected data an end device will transmit. This allows us to accumulate the energy of multiple symbols and decrease the SNR thresholds of standard LoRa even further, while neither additional hardware nor modifications on the end device are required. We run an evaluation using commodity hardware and different configurations to validate our approach. Our results show that our demodulation method provides an average SNR gain of 3.1 dB across different transmission settings on commodity hardware and is capable of demodulating packets with SNRs down to −23.1 dB.

Technology has rapidly evolved over the course of the last decades, and drastically transformed our way of life. Robots are no longer just mechanical aides, but have become collaborators on many tasks. Wearable gadgets have become virtually ubiquitous due to their ability to collect data, monitor health parameters, and assist users in various day-to-day tasks. In recent years, there has been a surge in interest around the use of wearable technologies to collect human psychological parameters for human–robot collaboration. With the field of robotics advancing, there is a growing need for robots to interact with humans seamlessly. To achieve this seamless human–robot connection, robots must be able to interpret human emotions and react appropriately. While understanding human emotions and behavior is a complex task in itself, wearable sensor systems contribute valuable insights. This survey provides a comprehensive overview of wearable gadgets and technologies proposed for measuring five key human factors — trust, cognitive workload, stress, safety perception, and fatigue — within the scope of human–robot collaboration, based on the systematic review of papers published between 2015 and the end of 2024 in six major databases. Our analysis indicates that trust and cognitive workload have received greater attention from researchers in recent years, as compared to other human factors. The Empatica E4 wristband, Shimmer3 GSR+ and EPOC X EEG headset are among the most widely used wearable devices, capable of capturing essential physiological parameters widely used for human–robot collaboration, including electrodermal activity, heart rate variability, skin temperature, and electroencephalogram. Besides reviewing the potentials and capabilities of these gadgets, we highlight their shortcomings and offer directions for future research in this domain.

Voice assistants, as provided by smart speakers, have become ubiquitous. Current authentication methods in these systems, however, rely on wake words, posing a risk due to the susceptibility to replay attacks. Additionally, user data stored on servers could expose sensitive information. This study suggests an approach to improve user authentication and profile management in smart speakers, reducing risks tied to external data processing and storage. We propose a two-fold solution for continuous user authentication and local user profiles. This approach prevents unauthorized access to sensitive data and grants users access to their local recordings. Our method differs from current practices in two ways: (1) It authenticates users based on complete voice commands, reducing the risk of replayed wake word attacks, and (2) it operates locally, avoiding the transfer of sensitive data to external servers. We offer a proof-of-concept with Alexa Voice Service (AVS) integration and a thorough evaluation using voice datasets and a study with 17 participants. We tested our approach under various conditions, including accents, background noise, and muffled speech. Legitimate users are identified with 93% precision, 95% recall, 94% F1-score, and 99% accuracy, while illegitimate users are recognized with 99% accuracy across these metrics.

In LoRaWANs, the distance between sensor devices and gateways can reach up to several kilometers. Determining whether a particular location within this range is suitable for the reliable operation of a wireless sensing device is, however, not trivial by means of theoretical analysis alone. Numerous factors besides the power at which a signal is transmitted, such as obstacles in the line-of-sight path, govern whether connectivity is given. Real-world deployments of LoRa devices are hence typically preceded by connectivity assessments using field test devices. This process, mostly realized by having a person move around a target zone to find the position that leads to the strongest LoRa signal reception at a gateway, is labor-intensive and needs to be repeated for every device to be newly rolled out. We demonstrate how this process can be automated by pre-populating LoRaWAN coverage maps with the help of bicycle riders, rather than taking measurements on-demand. We accomplish this through determining an optimal set of routes for bicyclists, to ride along all accessible tracks in an area of interest. By equipping cyclists with LoRa transmitters which periodically send out location beacons, receiving gateways can autonomously derive connectivity maps of their surrounding area. We visualize received signal strength values in the form of heatmaps, which can be used to make informed decisions about suitable deployment locations for additional sensors.

LoRa has become a de-facto standard for long-range communication networks with low data rate requirements. Despite the robustness of its employed chirp spread spectrum modulation, however, LoRa links covering long distances can be prone to errors and losses. A common reaction to such link conditions is to increase the symbol durations and thus their chance of a correct reception. In this paper, we follow an alternative approach to mitigate lossy links by adding redundant information to LoRa transmissions. We investigate the potentials of both local and collaborative methods to increase the network's overall data delivery. By means of simulation studies and a practical evaluation, for which we have implemented the designed techniques on actual LoRa hardware devices, we show that significant improvements are possible in a real-world deployment.

The deployment of embedded sensing devices on croplands and pastures is an enabling element for precision agriculture applications. Changing conditions (e.g., different crops being grown), however, may require the occasional reconfiguration of the resulting networks of wireless sensors, e.g., to modify data reporting rates or synchronize internal clocks. In this demo we showcase an opportunistic broadcast channel to forward such configuration messages to embedded systems. The transmitting station is realized by means of an electric fence energizer, a device frequently utilized in agricultural settings. On the receiver side, only little hardware efforts are required to capture the highvoltage pulses and decode transmitted configuration messages.

Wireless Sensor Networks establish the foundation for a revolution in precision agriculture. As an integral part of smart farming systems, they can collect detailed information about crop health, air and soil conditions, and other relevant parameters to support agriculturists in their decision-making. Likewise, decentralized actuation (e.g., opening sprinkler valves) becomes possible when embedded sensor and actuator devices are deployed. From a technical point of view, smart farming systems strongly rely on embedded devices with wireless communication interfaces to cater for their convenient deployment. The operation of their wireless radio transceivers, however, often represents a significant energetic burden. This is particularly conspicuous when compared to the low-power microcontrollers that have become ubiquitous on current-generation sensing systems. We mitigate this issue by following an entirely different approach in this work, namely by exploiting the presence of electric fence energizers that are widely used in farming scenarios. Our solution called PULSEHV modulates data onto the highvoltage electric pulses emitted by fence energizers and thus enables broadcast communications at no extra overhead. As electrical fences commonly encircle entire patches of cropland, they act as large sending antennas; a proximity between deployed sensing devices and this antenna is implicitly ensured thereby. We practically demonstrate how PulseHV accomplishes an effective data rate of 2.7 bit/s through the application of pulse position modulation. This limited throughput is counterbalanced by the fact that receiving the broadcast transmissions incurs virtually no energy overhead.

Several communication protocols based on channel hopping have been proposed for wireless sensor networks. One prominent example is IEEE 802.15.4e, which relies on the concept of time-slotted channel hopping (TSCH). TSCH effectively mitigates poor channel conditions (caused, e.g., by fading) by means of channel changes according to a pre-defined hopping sequence. In order for nodes to participate in TSCH networks, however, a synchronization procedure is necessary; to this end, newly booted nodes will listen on a default channel for beacons transmitted by nodes that are already synchronized. This process suffers from two drawbacks: Firstly, synchronized nodes do not continuously send beacons, but wait for a time period in-between their beacon transmissions. Secondly, in large networks, beacon collisions are very likely to occur, which also slow the association process down. In this paper, we study both impacts in simulated scenarios in order to derive potential for improvement.

Point-to-point traffic has emerged as a widely used communications paradigm for cyber-physical systems and wireless sensor networks in industrial settings. However, existing point-to-point communication protocols often entail substantial overhead to find and maintain reliable routes. In recent research, protocols that rely on the phenomenon of constructive interference have thus emerged. They allow to quickly, efficiently, and reliably flood packets to the entire network. As all nodes in the network need to (re-)broadcast all packets in such protocols by design, substantial energy is consumed by nodes that do not even contribute to the actual point-to-point transmission. We propose a novel point-to-point communication protocol, called RFT, which attempts to discover the most reliable route between a source and a destination. To achieve this objective, RFT selects the minimum number of participating nodes required to ensure reliable communications while allowing all other devices in the network to sleep. During data transmissions, the nodes on the direct route as well as all helper nodes broadcast the data packets and exploit the benefits of constructive interference in order to reduce end-to-end latency.

The technological development of inexpensive GPS receivers has enabled a new realm of applications for embedded sensing systems. The availability of location information allows these sensing system to study the motion trajectories of humans, animals, and objects. The storage of the collected trajectory data, however, represents a challenge for constrained devices with limited memory. In fact, external memory is often required, which incurs an additional cost for the storage component, enlarges the physical dimensions of the device, and also results in a measurable increase of the node's energy expenditure. In this paper, we present a clustering approach for GPS location information that is specifically tailored to resource-constrained sensing platforms. While our approach can be generalised to wide variety of applications, we focus on wireless animal tracking as an illustrative example. Our two-stage clustering process only records areas in which the animal has spent an extended period of time, in order to reduce the storage requirement while ensuring a low memory foot-print and processing requirements. We evaluate our solution using real-world animal GPS traces and show that our scheme achieves 90% improvement in location accuracy while also reducing the memory footprint by up to 99% in comparison with the state-of-the-art.

The ongoing success of smartphones and tablet computers, combined with the widespread deployment of cellular network infrastructure, has paved the way for ubiquitous Internet access. Access to mobile services has become a commodity for many commuters on public transport vehicles. On their daily trips to work and back, however, people often experience varying throughput rates due to the different capacities of network cells and the channel quality to the cell site. Links with reduced or no throughput are clearly unfavorable when users need to download large files or engage in synchronous communication activities. We thus introduce the notion of opportunistic personal bandwidth maps (OPBMs) in this paper. OPBMs allow the user to schedule activities with high throughput demand to parts of their journey where the bandwidth requirements are likely to be met. Users create their own OPBM by means of opportunistically monitoring their throughput during access to the cellular network and consolidating these individual measurements. Due to the opportunistic nature of our approach, no additional data transfers are required. Our measurements for more than 70 commutes show that the achievable throughput for road segments is highly variable across different trips. Still, the availability of OPBMs allows users to make decisions (e.g. to download a large file) when traveling along the segment with highest expected throughput.

Every day, we carry our mobile phone in our pocket or bag. When arriving at work or to a meeting, we may display it on the table. Most of the time, we however do not change the ringtone volume based on the new phone location. This may result in embarrassing situations when the volume is too loud or

The Internet of Things will encompass a rich variety of sensing systems including mobile phones, embedded sensor and actuator platforms, and even smart electricity meters. Through their collaborative operation, billions of such devices will realize the vision of smart homes, smart cities, and beyond

Traditionally, wireless sensor networks collect readings from distributed embedded sensing systems and forward them to one or more sink nodes. While many energy-efficient data collection protocols have emerged as a result, the transmission of control commands from a sink to individual nodes in the network is generally unsupported by these solutions. We present how the receiver-initiated opportunistic ORiNoCo data collection protocol can be extended to allow for the reconfiguration of nodes at minimal additional energy overhead. When our solution is being applied, adaptations of the sensor sampling rates or node sleep cycles can be easily controlled by the base station during runtime.

Current mobile sensing applications typically annotate the collected sensor readings with spatiotemporal information before reporting them to a central server. Such information can however endanger the users' privacy, as it reveals insights about their daily routines. Users must therefore trust the application administrators not to misuse the reported information. To diminish user dependence on administrators trustworthiness, we propose a privacy-preserving collaborative scheme, in which users exchange the collected sensor readings at opportunistic encounters. We model malicious administrators attempting to identify exchanged sensor readings based on spatial disparity by applying four state-of-the-art outlier detection algorithms. We thoroughly investigate the influence of different exchange patterns and the parameters of the algorithms on their performance based on a real-world dataset. The results for location traces of 20 users gathered during 14 days show that our algorithm achieves a high level of privacy protection.

Energy constitutes a scarce resource in wireless sensor networks, making energy-efficient operation mandatory. Data transmission has been identified as one of the most energy consuming operations. Consequently, different approaches to reduce data transmissions have been proposed, like data filtering. Recently, the value of information of sensor data has been identified for data filtering, explicitly incorporating application-specific and context-dependent information needs. The filtering is done according to the benefit a data transmission would induce at the recipient. We propose an on-mote filtering approach, which relies on local multi-step assessment of sensor data with forecasting and assessing value of information. We apply our approach to logistics transport processes and evaluate it concerning number of data transmissions and energy efficiency. Our simulation results showed that with our approach the number of data transmissions and the energy consumption can be reduced by over 25% to over 60%, while simultaneously accounting for user-specific information desires.

Platform heterogeneity in wireless sensor networks is often seen as a major challenge for application development. Once embedded systems with different processor architectures, computational power, and memory are part of the same network, algorithms and applications must be adapted to this additional degree of complexity. As a result, current sensor network deployments are (with exception of the sink node) commonly comprised of devices of identical make and model. In this paper, we show how device heterogeneity may be exploited to improve the energy efficiency of the sensor network by shifting resource-intensive processing tasks to other nodes within the network. To this end, we analyze the energy demand for representative processing operations and wireless communications on six heterogeneous state-of-the-art sensor platform types. Based on the created models, we assess the achievable energy savings when tasks are shifted to more powerful processing nodes. Our results show that platform heterogeneity, although often being perceived as a hindrance to the easy deployment of applications, also serves as an enabler for increased energy efficiency of the network.

IEEE 802.11s is an emerging standard for wireless mesh networks. Networks based on IEEE 802.11s directly benefit from existing security mechanisms in IEEE 802.11. This limits the attack surface of IEEE 802.11s significantly for adversaries that cannot authenticate with the network. Mesh networks are, however, often conceived for community network scenarios, which are inherently more open than managed infrastructure networks. This openness entails an increased risk of insider attacks, i.e., attacks by compromised stations that can authenticate with the network. Currently, IEEE 802.11s is lacking adequate protection against such insider attacks. In this paper, we hence derive an attack model for insider attacks and present two insider attack strategies to which IEEE 802.11s networks are prone, namely impairing the network performance and preventing communication between a pair of nodes. We design countermeasures that allow to defend the wireless network against both types of attacks. Our implementations only incur marginal computational and memory overheads, while the network security is measurably strengthened.

Energy-efficient operation is mandatory in wireless sensor networks due to the limited energy budget of sensor nodes. Considering the potential application of wireless sensor networks in logistics, cost efficiency is another major requirement due to high cost pressure. To save on data transmissions within such sensor deployments, which account for the major part of energy consumption and monetary costs, we develop a method for data filtering based on an in-network determination of transmission relevance of sensor data. Our approach explicitly incorporates interdependencies between wireless sensor nodes and their measurements and data transmissions. It contributes to efficiency in wireless sensor networks by filtering out irrelevant data and enables a subsequent reduction of unnecessary transmissions from a network-wide view, while being able to still offer real-time data provision with sufficient data fidelity to stakeholders. The benefits of our approach are indicated by preliminary evaluation results.

By leveraging smartphones as sensing platforms, mobile sensing applications can collect information in an unprecedented quantity and granularity. The transmission of unprocessed sensor readings can, however, pose severe threats to the users' privacy. To protect their privacy, users can apply filters to eliminate privacy-sensitive elements of the sensor readings prior to transmission. The resulting privacy protection depends on the configuration of these filters, which is controlled by the users through a privacy interface. In this paper, we study interface elements for the realization of this interface in order to foster its acceptance and maximize the efficacy of the provided privacy protection. To this end, we have implemented six graphical privacy interfaces, which have been evaluated by 80 participants of our user study. The results show a preference of the users towards differently colored and sized elements to visualize the current level of privacy protection and define their preferred privacy settings.

In tree-structured data collection sensor networks, packets are routed towards a sink node by iteratively choosing a node's immediate parent node as the next hop. It is however beyond the scope of these routing protocols to transfer messages along the reverse path, i.e., from the sink to individual nodes in the network. In this paper, we present CBFR, a novel routing scheme that builds upon collection protocols to enable efficient point-to-point communication. We propose the use of space-efficient data structures known as Bloom filters to efficiently store routing tables on the networked devices. In particular, each node in the collection tree stores the addresses of its direct and indirect child nodes in its local Bloom filter. A packet is forwarded down-tree only if the node's local filter indicates the presence of the packet's destination address among the node's descendants. In order to cater for the presence of mobile nodes, we apply the concept of counting Bloom filters to allow for the removal of elements from the filter by means of gradual forgetting. The effectiveness of our approach in achieving both high delivery rates and low overhead is demonstrated by means of simulations and experiments.

Most current smart metering solutions aim at in creasing user awareness for their household's electrical energy consumption. Although some smart meters make use of wireless data transfers between their sensor and display units, their integration into existing wireless sensor networks is hampered by proprietary communication interfaces and their lack of re programmability. Furthermore, the sole availability of aggregate consumption values renders current meters insufficient for novel application scenarios like smart home automation, for which information at device-level granularity and high resolution is vital. We address these shortcomings of existing solutions by presenting SmartMeter.KOM, our wireless sensor node capable of determining the current consumption of individual electrical appliances at high resolution. The platform is based on low-power hardware and incorporates a reprogrammable microcontroller which allows developers to easily deploy new algorithms. Its IEEE 802.15.4-compliant radio transceiver makes its integration with existing sensor networks possible, and thus enables their integration in smart buildings. We demonstrate the versatility of SmartMeter.KOM by presenting prototypical implementations of smart applications and identifying further research directions.

As wireless sensor nodes are mostly battery- powered, energy-efficient operation is a necessity to use their confined energy budget optimally. This is especially true in the logistics domain, where timely and accurate monitoring of containers is required, while the cost pressure is high. Thus, besides the need for energy efficiency, wireless sensor network deployments in logistics require cost efficiency as well. As data transmission represents the most expensive operation in terms of energy consumption and monetary costs, we present a concept for the local determination of transmission relevance in this paper. By omitting irrelevant events from transmission, the amount of data to transmit is effectively reduced. Our approach employs concepts from the business economics sector and is based on the use of scoresheets, which evaluate information on a wireless sensor node to decide whether they are "worth" transmitting or not. Thus, a scoresheet-based approach provides a viable solution for local filtering to realize energy-and cost-efficient operation of a wireless sensor network while maintaining the benefits of data fidelity and real-time event notifications.

The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications including, e.g., monitoring noise pollution or traffic and road conditions in urban environments. Data of unprecedented quantity and quality can be collected and reported by a possible user base of billions of mobile phone subscribers worldwide. The collection of detailed sensor and location data may however compromise user privacy. In this paper, we present a decentralized mechanism to preserve location privacy during the collection of sensor readings. As most sensor readings are geotagged, we propose to exchange them between users in physical proximity in order to jumble the paths followed by the users. We evaluate different strategies to exchange and report the sensor readings to the application using real-world GPS traces of mobile users. The results demonstrate the feasibility and efficacy of our proposed scheme, which can obfuscate up to 100% of the visited locations in the best instances.

The area of context-aware computing has received much attention in the last decade. However, many systems to determine a user’s context are focused on integrating a confined set of sensors into their decision-making algorithms, and lack extensibility for new and novel sources of context information. This limitation however has often resulted in monolithic software systems, reasoning on the user’s context from a static set of pre- defined rules. Therefore, most of these systems can not adapt to the user during runtime, and have not found wide adoption in reality. Unlike the existing approaches, we present a concept for a context-aware system circumventing these limitations by providing generic abstraction layers among its components. We show a concept for an extensible context-aware system with autonomous adaptation to changes in the user’s preferences, and provide arguments for our design decisions. After presenting the constraints for all participating entities, including sensors, middleware, and actuation and/or application frontends, we describe ContextFramework. KOM, an example implementation of the proposed concept.

Nodes in wireless sensor networks are generally confined in the energy budget available for their operation, hence energy-aware application design is mandatory to achieve long node lifetimes. Radio transmissions represent an inherent, but energetically costly characteristic of sensor networking. Significant reductions in the overall energy consumption can thus be achieved by reducing both the number of packet transmissions as well as the corresponding packet lengths. Data compression is a viable approach to conserve energy by increasing the information density within packets and thus transmitting shorter packets on the radio. We investigate the compression of packet headers in wireless sensor networks in this paper. Inspired by technologies used in the Internet and characteristics stemming from existing sensor network deployments, we propose a novel scheme for stateful header compression. Our scheme is specifically designed to consider both static and mobile leaf nodes, and can thus be applied in a majority of sensor network deployments. We analyze our scheme in different settings and show that its application leads to reductions of the required transmission energy and thus extended node lifetimes.

Recently, there has been much interest in positioning based on the widespreading WLAN technology, notably observed in the increasing number of hotspots and mobile devices equipped with WLAN interface. One technique to use WLAN for positioning is location fingerprinting, where WLAN networks in preselected sample locations are collected and used as fingerprints for those locations. However, to collect such fingerprints, existing services typically need to employ many skilled wardrivers who scan networks in the streets. This approach turns out to be very costly, especially when a large scale system coverage with acceptable positioning accuracy is required. Therefore, we propose WBroximity as a novel solution for the aforementioned concerns. With WBroximity, not only WLAN but also Bluetooth fingerprints are collected, therefore benefitting from the short range of Bluetooth to enable more precise positioning. Furthermore, such hybrid fingerprints are collected by using the paradigm of participatory sensing, thus cutting the extra costs needed to employ special personnel for this task, and allowing the system coverage to expand to wherever participants reach. In this paper, we present the technical details of realizing WBroximity as a location provider and its usage for collecting real fingerprint datasets. We evaluate the achieved accuracy in light of combining WLAN and Bluetooth, and the inherent aspects of participatory sensing, like number of participants and quality of participation. We give also an initial design and evaluation of a countermeasure to mitigate the effects of malicious participation.

Energy is limited in wireless sensor networks due to mostly battery-operated wireless sensor nodes. Consequently, an efficient energy usage significantly enhances the lifetime of a wireless sensor network. In this paper, we propose a cross-layer optimization concept that exploits business knowledge in the logistics domain to adapt communication by business relevancy. A corresponding information value is assessed for each detected event. Energy and communication costs can be saved by dropping or postponing the transmission of less relevant event information, without negative impact on the business and application level.

In wireless sensor networks, energy is commonly a scarce resource, which should be used as sparingly as possible to allow for long node lifetimes. It is therefore mandatory to put a focus on the development of energy-efficient applications. In this paper, we analyze the achievable energy gains when packet payloads are compressed prior to their transmission. As the radio transceiver chips are the predominant power consumers on most current sensor node platforms, we present how local compression of data can be successfully employed to preserve energy. We compare two lossless mechanisms to eliminate redundancies in the packets with regard to the overall energy savings. The results prove that data compression is a viable approach to reduce a platform's energy consumption, as it can reduce the radio transmission durations of packets and thus shorten the duty cycles of the radio device.

In wireless sensor networks, the energy consumption of participating nodes has crucial impact on the resulting network lifetime. Data compression is a viable approach towards preserving energy by reducing packet sizes and thus minimizing the activity periods of the radio transceiver. In this paper, we propose a compression framework utilizing a stream-oriented compression scheme for sensor networks. It is specifically tailored to the capabilities of employed nodes and network traffic characteristics, which we determine in a characterization of WSN traffic patterns. To mitigate the inapplicability of traditional compression approaches, we present the squeeze KOM compression layer. By shifting data compression into a dedicated layer, only minor modifications to applications are required, while efficient data transfer between nodes is provided. As a proof-of-concept, we implement a stream-based compression algorithm on sensor nodes and perform an experimental analysis to determine the potential gains under realistic traffic conditions. Results indicate that our presented lossless stream-oriented payload compression leads to considerable savings.

Future buildings and environments are envisioned to provide ambient intelligence, adapting to a userpsilas preferences based on information about his context and status. Smart heterogeneous sensor networks are well suited data sources for such environments, because they allow for dynamic adaptation to newly added sensor types and novel tasks. Realistic verification of protocols and algorithms for smart networks poses special constraints on testbeds, necessitating support for heterogeneous platforms and mobility in the network. These distinct requirements limit the usability of many known testbeds of purely homogeneous nature. In this paper, we determine a minimum set of premises for smart heterogeneous sensor network testbeds and evaluate existing architectures with respect to these requirements. We then present our tubicle node platform, an integrated sensor network node providing inherent support for heterogeneity and fulfilling the determined set of requirements in their entirety. A set of twenty tubicles forms the basis for our TWiNS.KOM testbed. Specifically designed for heterogeneity, the architecture allows rapid validation of smart sensor network algorithms and quick experimental setup.