## 6 Related Works  
In the traditional Internet, web services have always been put into relationship with the World Wide Web, hence they were hidden by web pages or queried by client-side applications. Originally, web services were heavyweight entities, thus not easily transferrable to devices with high constraints. Documents [25] and [26] demonstrated that it is reasonable to associate web services also to smart objects. The authors of [25] investigated service-oriented solutions that use _eXtensible Markup Language_ (XML) transactions enclosed into _Simple Object Access Protocol_ (SOAP) messages sent over HTTP and TCP. In [26], the authors presented an analysis of the REST transactions over HTTP. Document [17] defined a Web Linking by using a link format for constrained web servers to describe hosted resources. Moreover, in order to realize a _CORE Resource Discovery_, a well-known _Uniform Resource Identifier_ (URI)--well-known/core--is defined as a default entry-point for requesting the links hosted by a server. [27] defined a mechanism to register the notifications in case of resource state changes.  
In [22], the authors also point out that: (i) not all CoAP resource representations can fit into a single IEEE 802.15.4 frame, so that either 6 LoWPAN fragmentation or Blockwise transfer is required; (ii) the energy cost of Blockwise transfers would correspond to multiple requests with the payload size adjusted for the additional header option. Therefore, to enable energy-efficient transmissions of consecutive frames, the authors propose to use link-layer bursts. Basically, when a sender has several frames to send, it first wakes its neighbors up with a ContikiMAC strobe and sets the \"frame pending\" bit in the IEEE 802.15.4 frame header to tell the receiver that another frame will follow. Remaining frames are sent consecutively and acknowledged by the receiver until the frame pending bit is unset. This solution looks pretty interesting even if it is valid just for the IEEE 802.15.4 nodes. The solution we propose in the following sections is independent from the low layer technologies because it works at the _Network Level_.  
In the literature a reasoning similar to the one proposed in this paper, which is based on _non-human-centric specification_, can be found in some M2M solutions close to the _Semantic Web_ vision. In this vision, the information is machine processable and automated agents retrieve, extract, and combine it. In [28], authors explain how semantic technologies could play a key role in the Internet of Things. In particular, for their way of architecting the Web, the solutions based on _OWL Web Ontology Language_[29] (a standardized W3C technology) come very near to our proposal. Indeed, they are designed for applications that need to process the content of information, instead of just presenting information to humans. However, up to some years ago, OWL-based tools were too much complex and resource intensive for tiny nodes. In the recent years, research tried to expand _Semantic Web_ techniques and tools to deal with resource-constrained devices. Document [30] reviews the state of the art for the semantic specification of sensors (one of the fundamental technologies in the _semantic sensor network_ vision) which aims at solving difficulties in installing, querying and maintaining complex, heterogeneous sensor networks. In [31] authors developed an OWL _semantic reasoner_ for embedded devices.  
Authors of [32] and [33] present algorithms and encoding schemes for compressing representations. In particular, in [32] a coding scheme for _ontologies_ that embeds semantic awareness into devices with limited memory and processing capabilities is developed. This scheme provides a compact representation of an ontology and is enhanced with an efficient and effective semantic-matching algorithm. Finally, document [33] presented and evaluated an approach that saves memory without loss of reasoning ability, which facilitates OWL reasoning on constrained devices.